CREATE-IP and CREATE-V: Data and Services Update

NASA Astrophysics Data System (ADS)

Carriere, L.; Potter, G. L.; Hertz, J.; Peters, J.; Maxwell, T. P.; Strong, S.; Shute, J.; Shen, Y.; Duffy, D.

2017-12-01

The NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center and the Earth System Grid Federation (ESGF) are working together to build a uniform environment for the comparative study and use of a group of reanalysis datasets of particular importance to the research community. This effort is called the Collaborative REAnalysis Technical Environment (CREATE) and it contains two components: the CREATE-Intercomparison Project (CREATE-IP) and CREATE-V. This year's efforts included generating and publishing an atmospheric reanalysis ensemble mean and spread and improving the analytics available through CREATE-V. Related activities included adding access to subsets of the reanalysis data through ArcGIS and expanding the visualization tool to GMAO forecast data. This poster will present the access mechanisms to this data and use cases including example Jupyter Notebook code. The reanalysis ensemble was generated using two methods, first using standard Python tools for regridding, extracting levels and creating the ensemble mean and spread on a virtual server in the NCCS environment. The second was using a new analytics software suite, the Earth Data Analytics Services (EDAS), coupled with a high-performance Data Analytics and Storage System (DASS) developed at the NCCS. Results were compared to validate the EDAS methodologies, and the results, including time to process, will be presented. The ensemble includes selected 6 hourly and monthly variables, regridded to 1.25 degrees, with 24 common levels used for the 3D variables. Use cases for the new data and services will be presented, including the use of EDAS for the backend analytics on CREATE-V, the use of the GMAO forecast aerosol and cloud data in CREATE-V, and the ability to connect CREATE-V data to NCCS ArcGIS services.

Design and validation of MEDRYS, a Mediterranean Sea reanalysis over the period 1992-2013

NASA Astrophysics Data System (ADS)

Hamon, Mathieu; Beuvier, Jonathan; Somot, Samuel; Lellouche, Jean-Michel; Greiner, Eric; Jordà, Gabriel; Bouin, Marie-Noëlle; Arsouze, Thomas; Béranger, Karine; Sevault, Florence; Dubois, Clotilde; Drevillon, Marie; Drillet, Yann

2016-04-01

The French research community in the Mediterranean Sea modeling and the French operational ocean forecasting center Mercator Océan have gathered their skill and expertise in physical oceanography, ocean modeling, atmospheric forcings and data assimilation to carry out a MEDiterranean sea ReanalYsiS (MEDRYS) at high resolution for the period 1992-2013. The ocean model used is NEMOMED12, a Mediterranean configuration of NEMO with a 1/12° ( ˜ 7 km) horizontal resolution and 75 vertical z levels with partial steps. At the surface, it is forced by a new atmospheric-forcing data set (ALDERA), coming from a dynamical downscaling of the ERA-Interim atmospheric reanalysis by the regional climate model ALADIN-Climate with a 12 km horizontal and 3 h temporal resolutions. This configuration is used to carry a 34-year hindcast simulation over the period 1979-2013 (NM12-FREE), which is the initial state of the reanalysis in October 1992. MEDRYS uses the existing Mercator Océan data assimilation system SAM2 that is based on a reduced-order Kalman filter with a three-dimensional (3-D) multivariate modal decomposition of the forecast error. Altimeter data, satellite sea surface temperature (SST) and temperature and salinity vertical profiles are jointly assimilated. This paper describes the configuration we used to perform MEDRYS. We then validate the skills of the data assimilation system. It is shown that the data assimilation restores a good average temperature and salinity at intermediate layers compared to the hindcast. No particular biases are identified in the bottom layers. However, the reanalysis shows slight positive biases of 0.02 psu and 0.15 °C above 150 m depth. In the validation stage, it is also shown that the assimilation allows one to better reproduce water, heat and salt transports through the Strait of Gibraltar. Finally, the ability of the reanalysis to represent the sea surface high-frequency variability is shown.

Ecohydrological drought monitoring and prediction using a land data assimilation system

NASA Astrophysics Data System (ADS)

Sawada, Y.; Koike, T.

2017-12-01

Despite the importance of the ecological and agricultural aspects of severe droughts, few drought monitor and prediction systems can forecast the deficit of vegetation growth. To address this issue, we have developed a land data assimilation system (LDAS) which can simultaneously simulate soil moisture and vegetation dynamics. By assimilating satellite-observed passive microwave brightness temperature, which is sensitive to both surface soil moisture and vegetation water content, we can significantly improve the skill of a land surface model to simulate surface soil moisture, root zone soil moisture, and leaf area index (LAI). We run this LDAS to generate a global ecohydrological land surface reanalysis product. In this presentation, we will demonstrate how useful this new reanalysis product is to monitor and analyze the historical mega-droughts. In addition, using the analyses of soil moistures and LAI as initial conditions, we can forecast the ecological and hydrological conditions in the middle of droughts. We will present our recent effort to develop a near real time ecohydrological drought monitoring and prediction system in Africa by combining the LDAS and the atmospheric seasonal prediction.

Assessment of Uncertainties in Recent Changes in the South American Low-Level Jet Based on Various Reanalyses

NASA Astrophysics Data System (ADS)

Montini, T.; Jones, C.

2017-12-01

The South American low-level jet (SALLJ) is one of the key components of the South American Monsoon System. The SALLJ transports large amounts of moisture to the subtropics, influencing the development of deep convection and heavy precipitation over southeastern South America. Previous studies have analyzed the jet using reanalysis data due to the lack of available upper-air observations over this region. The purpose of the current study is to quantify uncertainties in the climatology, variability, and changes in the SALLJ based on various reanalyses for the period 1979-2015. This is important because there are significant differences among reanalysis datasets due to variations in their data quality control, data assimilation systems, and model physics. The datasets used in this analysis are: (1) Climate Forecast System Reanalysis, (2) ERA-Interim, (3) the Japanese 55-year reanalysis, (4) the Second Modern Era Retrospective-analysis for Research and Applications (MERRA-2). Finally, significant changes in the SALLJ are discussed in relation to substantial warming over South America in recent decades and changes in the monsoon.

Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica

NASA Astrophysics Data System (ADS)

Jones, R. W.; Renfrew, I. A.; Orr, A.; Webber, B. G. M.; Holland, D. M.; Lazzara, M. A.

2016-06-01

The glaciers within the Amundsen Sea Embayment (ASE), West Antarctica, are amongst the most rapidly retreating in Antarctica. Meteorological reanalysis products are widely used to help understand and simulate the processes causing this retreat. Here we provide an evaluation against observations of four of the latest global reanalysis products within the ASE region—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), Japanese 55-year Reanalysis (JRA-55), Climate Forecast System Reanalysis (CFSR), and Modern Era Retrospective-Analysis for Research and Applications (MERRA). The observations comprise data from four automatic weather stations (AWSs), three research vessel cruises, and a new set of 38 radiosondes all within the period 2009-2014. All four reanalyses produce 2 m temperature fields that are colder than AWS observations, with the biases varying from approximately -1.8°C (ERA-I) to -6.8°C (MERRA). Over the Amundsen Sea, spatially averaged summertime biases are between -0.4°C (JRA-55) and -2.1°C (MERRA) with notably larger cold biases close to the continent (up to -6°C) in all reanalyses. All four reanalyses underestimate near-surface wind speed at high wind speeds (>15 m s-1) and exhibit dry biases and relatively large root-mean-square errors (RMSE) in specific humidity. A comparison to the radiosonde soundings shows that the cold, dry bias at the surface extends into the lower troposphere; here ERA-I and CFSR reanalyses provide the most accurate profiles. The reanalyses generally contain larger temperature and humidity biases, (and RMSE) when a temperature inversion is observed, and contain larger wind speed biases (~2 to 3 m s-1), when a low-level jet is observed.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

NASA Astrophysics Data System (ADS)

Kishore, P.; Jyothi, S.; Basha, Ghouse; Rao, S. V. B.; Rajeevan, M.; Velicogna, Isabella; Sutterley, Tyler C.

2016-01-01

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial-temporal analyses of rainfall have been studied by using 107 (1901-2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

ECPC’s weekly to seasonal global forecasts

Treesearch

John O. Roads; Shyh-Chin Chen; Francis M. Fujioka

2001-01-01

The Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near-real-time seasonal global forecasts since 26 September 1997 with the NCEP global spectral model used for the reanalysis. Images of these forecasts, at daily to seasonal timescales, are provided on the World Wide Web and digital forecast products are provided on the ECPC...

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Multi-Spacecraft Data Assimilation and Reanalysis During the THEMIS and Van Allen Probes Era

NASA Astrophysics Data System (ADS)

Kellerman, A. C.; Shprits, Y.; Kondrashov, D. A.; Podladchikova, T.; Drozdov, A.; Subbotin, D.

2013-12-01

Earth's radiation belts are a dynamic system, controlled by competition between source, acceleration, loss and transport of particles. Solar wind pressure enhancements and outward transport are responsible for loss of electrons to the magnetopause, while wave-particle interactions inside the magnetosphere, driven by solar wind pressure and velocity variations, may lead to acceleration and radial diffusion of 10's of keV to MeV energy electrons, and pitch-angle scattering loss to the atmosphere. An understanding of the mechanisms behind the observed dynamics is critical to accurate modeling and hence forecasting of radiation belt conditions, important for design, and protection of our space-borne assets. The Versatile Electron Radiation Belt (VERB) model solves the Fokker-Planck diffusion equation in three dimensional invariant coordinates, which allows one to more effectively separate adiabatic and non-adiabatic changes in the radiation belt electron population. The model includes geomagnetic storm intensity dependent parameterizations of the following dominant magnetospheric waves: day- and night-side chorus, plasmaspheric hiss (in the inner magnetosphere and inside the plume region), lightning and anthropogenic generated waves, and electro-magnetic ion cyclotron (EMIC) waves, also inside of plasmaspheric plumes. The model is used to forecast the future state of the radiation belt electron population, while real-time data may be used to update the current state of the belts through assimilation with the model. The Kalman filter provides a computationally inexpensive method to assimilate data with a model, while taking into account the errors associated with each. System identification is performed to determine the model and observational bias and errors. The Kalman filter outputs an optimal estimate of the actual system state and the Kalman-gain weighted corrections (innovation) may be used to identify systematic differences between data and the model. Careful consideration of the innovation vector may lead to a new physical understanding of the radiation belt system, which can later be used to improve our model forecasts. In the current study, we explore the radiation belt dynamics of the current era including data from the THEMIS, Van Allen Probes, GPS satellites, Akebono, NOAA and Cluster spacecraft. Intercalibration is performed between spacecraft on an individual energy channel basis, and in invariant coordinates. The global reanalysis allows an unprecedented analysis of the source-acceleration-transport-loss relationship in Earth's radiation belts. This analysis is used to refine our model capabilities, and to prepare the 3-D reanalysis for real-time data. The global 3-D reanalysis is an important step towards full-scale modeling and operational forecasting of this dynamic region of space.

Improved Decadal Climate Prediction in the North Atlantic using EnOI-Assimilated Initial Condition

NASA Astrophysics Data System (ADS)

Li, Q.; Xin, X.; Wei, M.; Zhou, W.

2017-12-01

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1(BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal Oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

An analysis of simulated and observed storm characteristics

NASA Astrophysics Data System (ADS)

Benestad, R. E.

2010-09-01

A calculus-based cyclone identification (CCI) method has been applied to the most recent re-analysis (ERAINT) from the European Centre for Medium-range Weather Forecasts and results from regional climate model (RCM) simulations. The storm frequency for events with central pressure below a threshold value of 960-990hPa were examined, and the gradient wind from the simulated storm systems were compared with corresponding estimates from the re-analysis. The analysis also yielded estimates for the spatial extent of the storm systems, which was also included in the regional climate model cyclone evaluation. A comparison is presented between a number of RCMs and the ERAINT re-analysis in terms of their description of the gradient winds, number of cyclones, and spatial extent. Furthermore, a comparison between geostrophic wind estimated though triangules of interpolated or station measurements of SLP is presented. Wind still represents one of the more challenging variables to model realistically.

Using a Coupled Lake Model with WRF for Dynamical Downscaling

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

NASA Astrophysics Data System (ADS)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

Testing a Coupled Global-limited-area Data Assimilation System using Observations from the 2004 Pacific Typhoon Season

NASA Astrophysics Data System (ADS)

Holt, C. R.; Szunyogh, I.; Gyarmati, G.; Hoffman, R. N.; Leidner, M.

2011-12-01

Tropical cyclone (TC) track and intensity forecasts have improved in recent years due to increased model resolution, improved data assimilation, and the rapid increase in the number of routinely assimilated observations over oceans. The data assimilation approach that has received the most attention in recent years is Ensemble Kalman Filtering (EnKF). The most attractive feature of the EnKF is that it uses a fully flow-dependent estimate of the error statistics, which can have important benefits for the analysis of rapidly developing TCs. We implement the Local Ensemble Transform Kalman Filter algorithm, a vari- ation of the EnKF, on a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model and the NCEP Regional Spectral Model (RSM) to build a coupled global-limited area anal- ysis/forecast system. This is the first time, to our knowledge, that such a system is used for the analysis and forecast of tropical cyclones. We use data from summer 2004 to study eight tropical cyclones in the Northwest Pacific. The benchmark data sets that we use to assess the performance of our system are the NCEP Reanalysis and the NCEP Operational GFS analyses from 2004. These benchmark analyses were both obtained by the Statistical Spectral Interpolation, which was the operational data assimilation system of NCEP in 2004. The GFS Operational analysis assimilated a large number of satellite radiance observations in addition to the observations assimilated in our system. All analyses are verified against the Joint Typhoon Warning Center Best Track data set. The errors are calculated for the position and intensity of the TCs. The global component of the ensemble-based system shows improvement in po- sition analysis over the NCEP Reanalysis, but shows no significant difference from the NCEP operational analysis for most of the storm tracks. The regional com- ponent of our system improves position analysis over all the global analyses. The intensity analyses, measured by the minimum sea level pressure, are of similar quality in all of the analyses. Regional deterministic forecasts started from our analyses are generally not significantly different from those started from the GFS operational analysis. On average, the regional experiments performed better for longer than 48 h sea level pressure forecasts, while the global forecast performed better in predicting the position for longer than 48 h.

Status and Preliminary Evaluation for Chinese Re-Analysis Datasets

NASA Astrophysics Data System (ADS)

bin, zhao; chunxiang, shi; tianbao, zhao; dong, si; jingwei, liu

2016-04-01

Based on operational T639L60 spectral model, combined with Hybird_GSI assimilation system by using meteorological observations including radiosondes, buoyes, satellites el al., a set of Chinese Re-Analysis (CRA) datasets is developing by Chinese National Meteorological Information Center (NMIC) of Chinese Meteorological Administration (CMA). The datasets are run at 30km (0.28°latitude / longitude) resolution which holds higher resolution than most of the existing reanalysis dataset. The reanalysis is done in an effort to enhance the accuracy of historical synoptic analysis and aid to find out detailed investigation of various weather and climate systems. The current status of reanalysis is in a stage of preliminary experimental analysis. One-year forecast data during Jun 2013 and May 2014 has been simulated and used in synoptic and climate evaluation. We first examine the model prediction ability with the new assimilation system, and find out that it represents significant improvement in Northern and Southern hemisphere, due to addition of new satellite data, compared with operational T639L60 model, the effect of upper-level prediction is improved obviously and overall prediction stability is enhanced. In climatological analysis, compared with ERA-40, NCEP/NCAR and NCEP/DOE reanalyses, the results show that surface temperature simulates a bit lower in land and higher over ocean, 850-hPa specific humidity reflects weakened anomaly and the zonal wind value anomaly is focus on equatorial tropics. Meanwhile, the reanalysis dataset shows good ability for various climate index, such as subtropical high index, ESMI (East-Asia subtropical Summer Monsoon Index) et al., especially for the Indian and western North Pacific monsoon index. Latter we will further improve the assimilation system and dynamical simulating performance, and obtain 40-years (1979-2018) reanalysis datasets. It will provide a more comprehensive analysis for synoptic and climate diagnosis.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation

NASA Astrophysics Data System (ADS)

Benedetti, A.; Morcrette, J.-J.; Boucher, O.; Dethof, A.; Engelen, R. J.; Fisher, M.; Flentje, H.; Huneeus, N.; Jones, L.; Kaiser, J. W.; Kinne, S.; Mangold, A.; Razinger, M.; Simmons, A. J.; Suttie, M.

2009-07-01

This study presents the new aerosol assimilation system, developed at the European Centre for Medium-Range Weather Forecasts, for the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The aerosol modeling and analysis system is fully integrated in the operational four-dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalyses of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort. It focuses on the theoretical architecture and practical implementation of the aerosol assimilation system. It also provides a discussion of the background errors and observations errors for the aerosol fields, and presents a subset of results from the 2-year reanalysis which has been run for 2003 and 2004 using data from the Moderate Resolution Imaging Spectroradiometer on the Aqua and Terra satellites. Independent data sets are used to show that despite some compromises that have been made for feasibility reasons in regards to the choice of control variable and error characteristics, the analysis is very skillful in drawing to the observations and in improving the forecasts of aerosol optical depth.

Reanalysis comparisons of upper tropospheric-lower stratospheric jets and multiple tropopauses

NASA Astrophysics Data System (ADS)

Manney, Gloria L.; Hegglin, Michaela I.; Lawrence, Zachary D.; Wargan, Krzysztof; Millán, Luis F.; Schwartz, Michael J.; Santee, Michelle L.; Lambert, Alyn; Pawson, Steven; Knosp, Brian W.; Fuller, Ryan A.; Daffer, William H.

2017-09-01

The representation of upper tropospheric-lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution - for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry-climate models.

Comparison of trends and abrupt changes of the South Asia high from 1979 to 2014 in reanalysis and radiosonde datasets

NASA Astrophysics Data System (ADS)

Shi, Chunhua; Huang, Ying; Guo, Dong; Zhou, Shunwu; Hu, Kaixi; Liu, Yu

2018-05-01

The South Asian High (SAH) has an important influence on atmospheric circulation and the Asian climate in summer. However, current comparative analyses of the SAH are mostly between reanalysis datasets and there is a lack of sounding data. We therefore compared the climatology, trends and abrupt changes in the SAH in the Japanese 55-year Reanalysis (JRA-55) dataset, the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) dataset, the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-interim) dataset and radiosonde data from China using linear analysis and a sliding t-test. The trends in geopotential height in the control area of the SAH were positive in the JRA-55, NCEP-CFSR and ERA-interim datasets, but negative in the radiosonde data in the time period 1979-2014. The negative trends for the SAH were significant at the 90% confidence level in the radiosonde data from May to September. The positive trends in the NCEP-CFSR dataset were significant at the 90% confidence level in May, July, August and September, but the positive trends in the JRA-55 and ERA-Interim were only significant at the 90% confidence level in September. The reasons for the differences in the trends of the SAH between the radiosonde data and the three reanalysis datasets in the time period 1979-2014 were updates to the sounding systems, changes in instrumentation and improvements in the radiation correction method for calculations around the year 2000. We therefore analyzed the trends in the two time periods of 1979-2000 and 2001-2014 separately. From 1979 to 2000, the negative SAH trends in the radiosonde data mainly agreed with the negative trends in the NCEP-CFSR dataset, but were in contrast with the positive trends in the JRA-55 and ERA-Interim datasets. In 2001-2014, however, the trends in the SAH were positive in all four datasets and most of the trends in the radiosonde and NCEP-CFSR datasets were significant. It is therefore better to use the NCEP-CFSR dataset than the JRA-55 and ERA-Interim datasets when discussing trends in the SAH.

An assessment of historical Antarctic precipitation and temperature trend using CMIP5 models and reanalysis datasets

NASA Astrophysics Data System (ADS)

Tang, Malcolm S. Y.; Chenoli, Sheeba Nettukandy; Samah, Azizan Abu; Hai, Ooi See

2018-03-01

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts "Interim" reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979-2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year-1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.

Mid-latitude storm track variability and its influence on atmospheric composition

NASA Astrophysics Data System (ADS)

Knowland, K. E.; Doherty, R. M.; Hodges, K.

2013-12-01

Using the storm tracking algorithm, TRACK (Hodges, 1994, 1995, 1999), we have studied the behaviour of storm tracks in the North Atlantic basin, using 850-hPa relative vorticity from the ERA-Interim Re-analysis (Dee et al., 2011). We have correlated surface ozone measurements at rural coastal sites in Europe to the storm track data to explore the role mid-latitude cyclones and their transport of pollutants play in determining surface air quality in Western Europe. To further investigate this relationship, we have used the Monitoring Atmospheric Composition Climate (MACC) Re-analysis dataset (Inness et al., 2013) in TRACK. The MACC Re-analysis is a 10-year dataset which couples a chemistry transport model (Mozart-3; Stein 2009, 2012) to an extended version of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS). Storm tracks in the MACC Re-analysis compare well to the storm tracks using the ERA-Interim Re-analysis for the same 10-year period, as both are based on ECMWF IFSs. We also compare surface ozone values from MACC to surface ozone measurements previously studied. Using TRACK, we follow ozone (O3) and carbon monoxide (CO) through the life cycle of storms from North America to Western Europe. Along the storm tracks, we examine the distribution of CO and O3 within 6 degrees of the center of each storm and vertically at different pressure levels in the troposphere. We hope to better understand the mechanisms with which pollution is vented from the boundary layer to the free troposphere, as well as transport of pollutants to rural areas. Our hope is to give policy makers more detailed information on how climate variability associated with storm tracks between 1979-2013 may affect air quality in Northeast USA and Western Europe.

Precipitation and floodiness

NASA Astrophysics Data System (ADS)

Stephens, E.; Day, J. J.; Pappenberger, F.; Cloke, H.

2015-12-01

There are a number of factors that lead to nonlinearity between precipitation anomalies and flood hazard; this nonlinearity is a pertinent issue for applications that use a precipitation forecast as a proxy for imminent flood hazard. We assessed the degree of this nonlinearity for the first time using a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010). We introduced new indices to assess large-scale flood hazard, or floodiness, and quantified the link between monthly precipitation, river discharge, and floodiness anomalies at the global and regional scales. The results show that monthly floodiness is not well correlated with precipitation, therefore demonstrating the value of hydrometeorological systems for providing floodiness forecasts for decision-makers. A method is described for forecasting floodiness using the Global Flood Awareness System, building a climatology of regional floodiness from which to forecast floodiness anomalies out to 2 weeks.

Oceanic sources of predictability for MJO propagation across the Maritime Continent in a subset of S2S forecast models

NASA Astrophysics Data System (ADS)

DeMott, C. A.; Klingaman, N. P.

2017-12-01

Skillful prediction of the Madden-Julian oscillation (MJO) passage across the Maritime Continent (MC) has important implications for global forecasts of high-impact weather events, such as atmospheric rivers and heat waves. The North American teleconnection response to the MJO is strongest when MJO convection is located in the western Pacific Ocean, but many climate and forecast models are deficient in their simulation of MC-crossing MJO events. Compared to atmosphere-only general circulation models (AGCMs), MJO simulation skill generally improves with the addition of ocean feedbacks in coupled GCMs (CGCMs). Using observations, previous studies have noted that the degree of ocean coupling may vary considerably from one MJO event to the next. The coupling mechanisms may be linked to the presence of ocean Equatorial Rossby waves, the sign and amplitude of Equatorial surface currents, and the upper ocean temperature and salinity profiles. In this study, we assess the role of ocean feedbacks to MJO prediction skill using a subset of CGCMs participating in the Subseasonal-to-Seasonal (S2S) Project database. Oceanic observational and reanalysis datasets are used to characterize the upper ocean background state for observed MJO events that do and do not propagate beyond the MC. The ability of forecast models to capture the oceanic influence on the MJO is first assessed by quantifying SST forecast skill. Next, a set of previously developed air-sea interaction diagnostics is applied to model output to measure the role of SST perturbations on the forecast MJO. The "SST effect" in forecast MJO events is compared to that obtained from reanalysis data. Leveraging all ensemble members of a given forecast helps disentangle oceanic model biases from atmospheric model biases, both of which can influence the expression of ocean feedbacks in coupled forecast systems. Results of this study will help identify areas of needed model improvement for improved MJO forecasts.

Comparison of Two Global Ocean Reanalyses, NRL Global Ocean Forecast System (GOFS) and U. Maryland Simple Ocean Data Assimilation (SODA)

NASA Astrophysics Data System (ADS)

Richman, J. G.; Shriver, J. F.; Metzger, E. J.; Hogan, P. J.; Smedstad, O. M.

2017-12-01

The Oceanography Division of the Naval Research Laboratory recently completed a 23-year (1993-2015) coupled ocean-sea ice reanalysis forced by NCEP CFS reanalysis fluxes. The reanalysis uses the Global Ocean Forecast System (GOFS) framework of the HYbrid Coordinate Ocean Model (HYCOM) and the Los Alamos Community Ice CodE (CICE) and the Navy Coupled Ocean Data Assimilation 3D Var system (NCODA). The ocean model has 41 layers and an equatorial resolution of 0.08° (8.8 km) on a tri-polar grid with the sea ice model on the same grid that reduces to 3.5 km at the North Pole. Sea surface temperature (SST), sea surface height (SSH) and temperature-salinity profile data are assimilated into the ocean every day. The SSH anomalies are converted into synthetic profiles of temperature and salinity prior to assimilation. Incremental analysis updating of geostrophically balanced increments is performed over a 6-hour insertion window. Sea ice concentration is assimilated into the sea ice model every day. Following the lead of the Ocean Reanalysis Intercomparison Project (ORA-IP), the monthly mean upper ocean heat and salt content from the surface to 300 m, 700m and 1500 m, the mixed layer depth, the depth of the 20°C isotherm, the steric sea surface height and the Atlantic Meridional Overturning Circulation for the GOFS reanalysis and the Simple Ocean Data Assimilation (SODA 3.3.1) eddy-permitting reanalysis have been compared on a global uniform 0.5° grid. The differences between the two ocean reanalyses in heat and salt content increase with increasing integration depth. Globally, GOFS trends to be colder than SODA at all depth. Warming trends are observed at all depths over the 23 year period. The correlation of the upper ocean heat content is significant above 700 m. Prior to 2004, differences in the data assimilated lead to larger biases. The GOFS reanalysis assimilates SSH as profile data, while SODA doesn't. Large differences are found in the Western Boundary Currents, Southern Ocean and equatorial regions. In the Indian Ocean, the Equatorial Counter Current extends to far to the east and the subsurface flow in the thermocline is too weak in GOFS. The 20°C isotherm is biased 2 m shallow in SODA compared to GOFS, but the monthly anomalies in the depth are highly correlated.

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Reconstructing Common Era Climate Fields Using Data Assimilation, Proxies, and a Linear Climate Model

NASA Astrophysics Data System (ADS)

Perkins, W. A.; Hakim, G. J.

2016-12-01

In this work, we examine the skill of a new approach to performing climate field reconstructions (CFRs) using a form of online paleoclimate data assimilation (PDA). Many previous studies have foregone climate model forecasts during assimilation due to the computational expense of running coupled global climate models (CGCMs), and the relatively low skill of these forecasts on longer timescales. Here we greatly diminish the computational costs by employing an empirical forecast model (known as a linear inverse model; LIM), which has been shown to have comparable skill to CGCMs. CFRs of annually averaged 2m air temperature anomalies are compared between the Last Millennium Reanalysis framework (no forecasting or "offline"), a persistence forecast, and four LIM forecasting experiments over the instrumental period (1850 - 2000). We test LIM calibrations for observational (Berkeley Earth), reanalysis (20th Century Reanalysis), and CMIP5 climate model (CCSM4 and MPI) data. Generally, we find that the usage of LIM forecasts for online PDA increases reconstruction agreement with the instrumental record for both spatial and global mean temperature (GMT). The detrended GMT skill metrics show the most dramatic increases in skill with coefficient of efficiency (CE) improvements over the no-forecasting benchmark averaging 57%. LIM experiments display a common pattern of spatial field increases in CE skill over northern hemisphere land areas and in the high-latitude North Atlantic - Barents Sea corridor (Figure 1). However, the non-GCM-calibrated LIMs introduce other deficiencies into the spatial skill of these reconstructions, likely due to aspects of the LIM calibration process. Overall, the CMIP5 LIMs have the best performance when considering both spatial fields and GMT. A comparison with the persistence forecast experiment suggests that improvements are associated with the usage of the LIM forecasts, and not simple persistence of temperature anomalies over time. These results show that the use of LIM forecasting can help add further dynamical constraint to CFRs. As we move forward, this will be an important factor in fully utilizing dynamically consistent information from the proxy record while reconstructing the past millennium.

An ocean data assimilation system and reanalysis of the World Ocean hydrophysical fields

NASA Astrophysics Data System (ADS)

Zelenko, A. A.; Vil'fand, R. M.; Resnyanskii, Yu. D.; Strukov, B. S.; Tsyrulnikov, M. D.; Svirenko, P. I.

2016-07-01

A new version of the ocean data assimilation system (ODAS) developed at the Hydrometcentre of Russia is presented. The assimilation is performed following the sequential scheme analysis-forecast-analysis. The main components of the ODAS are procedures for operational observation data processing, a variational analysis scheme, and an ocean general circulation model used to estimate the first guess fields involved in the analysis. In situ observations of temperature and salinity in the upper 1400-m ocean layer obtained from various observational platforms are used as input data. In the new ODAS version, the horizontal resolution of the assimilating model and of the output products is increased, the previous 2D-Var analysis scheme is replaced by a more general 3D-Var scheme, and a more flexible incremental analysis updating procedure is introduced to correct the model calculations. A reanalysis of the main World Ocean hydrophysical fields over the 2005-2015 period has been performed using the updated ODAS. The reanalysis results are compared with data from independent sources.

Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Dasilva, Arindo M.

2012-01-01

Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.

Web-GIS approach for integrated analysis of heterogeneous georeferenced data

NASA Astrophysics Data System (ADS)

Okladnikov, Igor; Gordov, Evgeny; Titov, Alexander; Shulgina, Tamara

2014-05-01

Georeferenced datasets are currently actively used for modeling, interpretation and forecasting of climatic and ecosystem changes on different spatial and temporal scales [1]. Due to inherent heterogeneity of environmental datasets as well as their huge size (up to tens terabytes for a single dataset) a special software supporting studies in the climate and environmental change areas is required [2]. Dedicated information-computational system for integrated analysis of heterogeneous georeferenced climatological and meteorological data is presented. It is based on combination of Web and GIS technologies according to Open Geospatial Consortium (OGC) standards, and involves many modern solutions such as object-oriented programming model, modular composition, and JavaScript libraries based on GeoExt library (http://www.geoext.org), ExtJS Framework (http://www.sencha.com/products/extjs) and OpenLayers software (http://openlayers.org). The main advantage of the system lies in it's capability to perform integrated analysis of time series of georeferenced data obtained from different sources (in-situ observations, model results, remote sensing data) and to combine the results in a single map [3, 4] as WMS and WFS layers in a web-GIS application. Also analysis results are available for downloading as binary files from the graphical user interface or can be directly accessed through web mapping (WMS) and web feature (WFS) services for a further processing by the user. Data processing is performed on geographically distributed computational cluster comprising data storage systems and corresponding computational nodes. Several geophysical datasets represented by NCEP/NCAR Reanalysis II, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis, ECMWF ERA Interim Reanalysis, MRI/JMA APHRODITE's Water Resources Project Reanalysis, DWD Global Precipitation Climatology Centre's data, GMAO Modern Era-Retrospective analysis for Research and Applications, reanalysis of Monitoring atmospheric composition and climate (MACC) Collaborated Project, NOAA-CIRES Twentieth Century Global Reanalysis Version II, NCEP Climate Forecast System Reanalysis (CFSR), meteorological observational data for the territory of the former USSR for the 20th century, results of modeling by global and regional climatological models, and others are available for processing by the system. The Web-GIS information-computational system for heterogeneous geophysical data analysis provides specialists involved into multidisciplinary research projects with reliable and practical instruments for integrated research of climate and ecosystems changes on global and regional scales. With its help even an unskilled in programming user is able to process and visualize multidimensional observational and model data through unified web-interface using a common graphical web-browser. This work is partially supported by SB RAS project VIII.80.2.1, RFBR grant #13-05-12034, grant #14-05-00502, and integrated project SB RAS #131. References 1. Gordov E.P., Lykosov V.N., Krupchatnikov V.N., Okladnikov I.G., Titov A.G., Shulgina T.M. Computational and information technologies for monitoring and modeling of climate changes and their consequences. - Novosibirsk: Nauka, Siberian branch, 2013. - 195 p. (in Russian) 2. Felice Frankel, Rosalind Reid. Big data: Distilling meaning from data // Nature. Vol. 455. N. 7209. P. 30. 3. T.M. Shulgina, E.P. Gordov, I.G. Okladnikov, A.G., Titov, E.Yu. Genina, N.P. Gorbatenko, I.V. Kuzhevskaya, A.S. Akhmetshina. Software complex for a regional climate change analysis. // Vestnik NGU. Series: Information technologies. 2013. Vol. 11. Issue 1. P. 124-131 (in Russian). 4. I.G. Okladnikov, A.G. Titov, T.M. Shulgina, E.P. Gordov, V.Yu. Bogomolov, Yu.V. Martynova, S.P. Suschenko, A.V. Skvortsov. Software for analysis and visualization of climate change monitoring and forecasting data // Numerical methods and programming, 2013. Vol. 14. P. 123-131 (in Russian).

Thirty-four years of Hawaii wave hindcast from downscaling of climate forecast system reanalysis

NASA Astrophysics Data System (ADS)

Li, Ning; Cheung, Kwok Fai; Stopa, Justin E.; Hsiao, Feng; Chen, Yi-Leng; Vega, Luis; Cross, Patrick

2016-04-01

The complex wave climate of Hawaii includes a mix of seasonal swells and wind waves from all directions across the Pacific. Numerical hindcasting from surface winds provides essential space-time information to complement buoy and satellite observations for studies of the marine environment. We utilize WAVEWATCH III and SWAN (Simulating WAves Nearshore) in a nested grid system to model basin-wide processes as well as high-resolution wave conditions around the Hawaiian Islands from 1979 to 2013. The wind forcing includes the Climate Forecast System Reanalysis (CFSR) for the globe and downscaled regional winds from the Weather Research and Forecasting (WRF) model. Long-term in-situ buoy measurements and remotely-sensed wind speeds and wave heights allow thorough assessment of the modeling approach and data products for practical application. The high-resolution WRF winds, which include orographic and land-surface effects, are validated with QuickSCAT observations from 2000 to 2009. The wave hindcast reproduces the spatial patterns of swell and wind wave events detected by altimeters on multiple platforms between 1991 and 2009 as well as the seasonal variations recorded at 16 offshore and nearshore buoys around the Hawaiian Islands from 1979 to 2013. The hindcast captures heightened seas in interisland channels and around prominent headlands, but tends to overestimate the heights of approaching northwest swells and give lower estimates in sheltered areas. The validated high-resolution hindcast sets a baseline for future improvement of spectral wave models.

Multi-Reanalysis Comparison of Variability in Analysis Increment of Column-Integrated Water Vapor Associated with Madden-Julian Oscillation

NASA Astrophysics Data System (ADS)

Yokoi, S.

2014-12-01

This study conducts a comparison of three reanalysis products (JRA-55, JRA-25, and ERA-Interim) in representation of Madden-Julian Oscillation (MJO), focusing on column-integrated water vapor (CWV) that is considered as an essential variable for discussing MJO dynamics. Besides the analysis fields of CWV, which exhibit spatio-temporal distributions that are quite similar to satellite observations, CWV tendency simulated by forecast models and analysis increment calculated by data assimilation are examined. For JRA-55, it is revealed that, while its forecast model is able to simulate eastward propagation of the CWV anomaly, it tends to weaken the amplitude, and data assimilation process sustains the amplitude. The multi-reanalysis comparison of the analysis increment further reveals that this weakening bias is probably caused by excessively weak cloud-radiative feedback represented by the model. This bias in the feedback strength makes anomalous moisture supply by the vertical advection term in the CWV budget equation too insensitive to precipitation anomaly, resulting in reduction of the amplitude of CWV anomaly. ERA-Interim has a nearly opposite feature; the forecast model represents excessively strong feedback and unrealistically strengthens the amplitude, while the data assimilation weakens it. These results imply the necessity of accurate representation of the cloud-radiative feedback strength for a short-term MJO forecast, and may be evidence to support the argument that this feedback is essential for the existence of MJO. Furthermore, this study demonstrates that the multi-reanalysis comparison of the analysis increment will provide useful information for identifying model biases and, potentially, for estimating parameters that are difficult to estimate solely from observation data, such as gross moist stability.

Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

NASA Astrophysics Data System (ADS)

Beria, H.; Nanda, T., Sr.; Chatterjee, C.

2015-12-01

High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

Development of an eddy-resolving reanalysis using the 1/12° global HYbrid Coordinate Ocean Model and the Navy Coupled Ocean Data Assimilation Scheme

NASA Astrophysics Data System (ADS)

Allard, Richard; Metzger, E. Joseph; Broome, Robert; Franklin, Deborah; Smedstad, Ole Martin; Wallcraft, Alan

2013-04-01

Multiple international agencies have performed atmospheric reanalyses using static dynamical models and assimilation schemes while ingesting all available quality controlled observational data. Some are clearly aimed at climate time scales while others focus on the more recent time period in which assimilated satellite data are used to constrain the system. Typically these are performed at horizontal and vertical resolutions that are coarser than the existing operational atmospheric prediction system. Multiple agencies have also performed ocean reanalyses using some of the atmospheric forcing products described above. However, only a few are eddy-permitting and none are capable of resolving oceanic mesoscale features (eddies and current meanders) across the entire globe. To fill this void, the Naval Research Laboratory is performing an eddy-resolving 1993-2010 ocean reanalysis using the 1/12° global HYbrid Coordinate Ocean Model (HYCOM) that employs the Navy Coupled Ocean Data Assimilation (NCODA) scheme. A 1/12° global HYCOM/NCODA prediction system has been running in real-time at the Naval Oceanographic Office (NAVOCEANO) since 22 December 2006. It has undergone operational testing and will become an operational product by early 2013. It is capable of nowcasting and forecasting the oceanic "weather" which includes the 3D ocean temperature, salinity and current structure, the surface mixed layer, and the location of mesoscale features such as eddies, meandering currents and fronts. The system has a mid-latitude resolution of ~7 km and employs 32 hybrid vertical coordinate surfaces. Compared to traditional isopycnal coordinate models, the hybrid vertical coordinate extends the geographic range of applicability toward shallow coastal seas and the unstratified parts of the world ocean. HYCOM contains a built-in thermodynamic ice model, where ice grows and melts due to heat flux and sea surface temperature (SST) changes, but it does not contain advanced rheological physics. The ice edge is constrained by satellite ice concentration. Once per day, NCODA performs a 3D ocean analysis using all available observational data and the 1-day HYCOM forecast as the first guess in a sequential incremental update cycle. Observational data include surface observations from satellites, including sea surface height (SSH) anomalies, SST, and sea ice concentrations, plus in-situ SST observations from ships and buoys as well as temperature and salinity profiles from XBTs, CTDs and Argo profiling floats. Surface information is projected downward using synthetic profiles from the Modular Ocean Data Assimilation System (MODAS) at those locations with a predefined SSH anomaly. Unlike previous reanalyses, this ocean reanalysis will be integrated at the same horizontal and vertical resolution as the operational system running at NAVOCEANO. The system is forced with atmospheric output from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) and the observations listed above. The reanalysis began in 1993 because of the advent of satellite altimeter data that will constrain the oceanic mesoscale. Significant effort has been put into obtaining and quality controlling all input observational data, with special emphasis on the profile data. The computational resources are obtained through the High Performance Computing Modernization Office.

GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

NASA Astrophysics Data System (ADS)

Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

2017-04-01

Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal outlook while providing useful information to users and partners. We demonstrate the first version of an operational GloFAS seasonal outlook, outlining the model set-up and presenting a first look at the seasonal forecasts that will be displayed in the GloFAS interface, and discuss the initial results of the forecast evaluation.

Extended and refined multi sensor reanalysis of total ozone for the period 1970-2012

NASA Astrophysics Data System (ADS)

van der A, R. J.; Allaart, M. A. F.; Eskes, H. J.

2015-07-01

The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite data sets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6-hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite data sets are collected and then are corrected for biases as a function of solar zenith angle (SZA), viewing zenith angle (VZA), time (trend), and stratospheric temperature using surface observations of the ozone column from Brewer and Dobson spectrophotometers from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Subsequently the de-biased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM. The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43-year period 1970-2012. The chemistry transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. Backscatter ultraviolet (BUV) satellite observations have been included for the period 1970-1977. The total record is extended by 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° × 3° to 1° × 1°. The analysis is driven by 3-hourly meteorology from the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1 % with respect to de-biased satellite observations after 1979.

JRAero: the Japanese Reanalysis for Aerosol v1.0

NASA Astrophysics Data System (ADS)

Yumimoto, Keiya; Tanaka, Taichu Y.; Oshima, Naga; Maki, Takashi

2017-09-01

A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0. 90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

The Impacts of Climate Variations on Military Operations in the Horn of Africa

DTIC Science & Technology

2006-03-01

variability in a region. Climate forecasts are predictions of the future state of the climate , much as we think of weather forecasts but at longer...arrive at accurate characterizations of the future state of the climate . Many of the civilian organizations that generate reanalysis data also

Multi-centennial upper-ocean heat content reconstruction using online data assimilation

NASA Astrophysics Data System (ADS)

Perkins, W. A.; Hakim, G. J.

2017-12-01

The Last Millennium Reanalysis (LMR) provides an advanced paleoclimate ensemble data assimilation framework for multi-variate climate field reconstructions over the Common Era. Although reconstructions in this framework with full Earth system models remain prohibitively expensive, recent work has shown improved ensemble reconstruction validation using computationally inexpensive linear inverse models (LIMs). Here we leverage these techniques in pursuit of a new multi-centennial field reconstruction of upper-ocean heat content (OHC), synthesizing model dynamics with observational constraints from proxy records. OHC is an important indicator of internal climate variability and responds to planetary energy imbalances. Therefore, a consistent extension of the OHC record in time will help inform aspects of low-frequency climate variability. We use the Community Climate System Model version 4 (CCSM4) and Max Planck Institute (MPI) last millennium simulations to derive the LIMs, and the PAGES2K v.2.0 proxy database to perform annually resolved reconstructions of upper-OHC, surface air temperature, and wind stress over the last 500 years. Annual OHC reconstructions and uncertainties for both the global mean and regional basins are compared against observational and reanalysis data. We then investigate differences in dynamical behavior at decadal and longer time scales between the reconstruction and simulations in the last-millennium Coupled Model Intercomparison Project version 5 (CMIP5). Preliminary investigation of 1-year forecast skill for an OHC-only LIM shows largely positive spatial grid point local anomaly correlations (LAC) with a global average LAC of 0.37. Compared to 1-year OHC persistence forecast LAC (global average LAC of 0.30), the LIM outperforms the persistence forecasts in the tropical Indo-Pacific region, the equatorial Atlantic, and in certain regions near the Antarctic Circumpolar Current. In other regions, the forecast correlations are less than the persistence case but still positive overall.

Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Petropoulos, George P.; Gupta, Manika; Dai, Qiang

2016-04-01

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = -7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = -10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003-2015

NASA Astrophysics Data System (ADS)

Flemming, Johannes; Benedetti, Angela; Inness, Antje; Engelen, Richard J.; Jones, Luke; Huijnen, Vincent; Remy, Samuel; Parrington, Mark; Suttie, Martin; Bozzo, Alessio; Peuch, Vincent-Henri; Akritidis, Dimitris; Katragkou, Eleni

2017-02-01

A new global reanalysis data set of atmospheric composition (AC) for the period 2003-2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

Sensitivity of Historical Simulation of the Permafrost to Different Atmospheric Forcing Data Sets from 1979 to 2009

NASA Astrophysics Data System (ADS)

Guo, Donglin; Wang, Huijun; Wang, Aihui

2017-11-01

Numerical simulation is of great importance to the investigation of changes in frozen ground on large spatial and long temporal scales. Previous studies have focused on the impacts of improvements in the model for the simulation of frozen ground. Here the sensitivities of permafrost simulation to different atmospheric forcing data sets are examined using the Community Land Model, version 4.5 (CLM4.5), in combination with three sets of newly developed and reanalysis-based atmospheric forcing data sets (NOAA Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-I), and NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA)). All three simulations were run from 1979 to 2009 at a resolution of 0.5° × 0.5° and validated with what is considered to be the best available permafrost observations (soil temperature, active layer thickness, and permafrost extent). Results show that the use of reanalysis-based atmospheric forcing data set reproduces the variations in soil temperature and active layer thickness but produces evident biases in their climatologies. Overall, the simulations based on the CFSR and ERA-I data sets give more reasonable results than the simulation based on the MERRA data set, particularly for the present-day permafrost extent and the change in active layer thickness. The three simulations produce ranges for the present-day climatology (permafrost area: 11.31-13.57 × 106 km2; active layer thickness: 1.10-1.26 m) and for recent changes (permafrost area: -5.8% to -9.0%; active layer thickness: 9.9%-20.2%). The differences in air temperature increase, snow depth, and permafrost thermal conditions in these simulations contribute to the differences in simulated results.

A comparison of Loon balloon observations and stratospheric reanalysis products

NASA Astrophysics Data System (ADS)

Friedrich, Leon S.; McDonald, Adrian J.; Bodeker, Gregory E.; Cooper, Kathy E.; Lewis, Jared; Paterson, Alexander J.

2017-01-01

Location information from long-duration super-pressure balloons flying in the Southern Hemisphere lower stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses including National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP-CFSv2), European Centre for Medium-Range Weather Forecasts (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and the recently released MERRA version 2. Balloon GPS location information is used to derive wind speeds which are then compared with values from the reanalyses interpolated to the balloon times and locations. All reanalysis data sets accurately describe the winds, with biases in zonal winds of less than 0.37 m s-1 and meridional biases of less than 0.08 m s-1. The standard deviation on the differences between Loon and reanalyses zonal winds is latitude-dependent, ranging between 2.5 and 3.5 m s-1, increasing equatorward. Comparisons between Loon trajectories and those calculated by applying a trajectory model to reanalysis wind fields show that MERRA-2 wind fields result in the most accurate simulated trajectories with a mean 5-day balloon-reanalysis trajectory separation of 621 km and median separation of 324 km showing significant improvements over MERRA version 1 and slightly outperforming ERA-Interim. The latitudinal structure of the trajectory statistics for all reanalyses displays marginally lower mean separations between 15 and 35° S than between 35 and 55° S, despite standard deviations in the wind differences increasing toward the equator. This is shown to be related to the distance travelled by the balloon playing a role in the separation statistics.

Providing Access to a Diverse Set of Global Reanalysis Dataset Collections

NASA Astrophysics Data System (ADS)

Schuster, D.; Worley, S. J.

2015-12-01

The National Center for Atmospheric Research (NCAR) Research Data Archive (RDA, http://rda.ucar.edu) provides open access to a variety of global reanalysis dataset collections to support atmospheric and related sciences research worldwide. These include products from the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), and NCAR.All RDA hosted reanalysis collections are freely accessible to registered users through a variety of methods. Standard access methods include traditional browser and scripted HTTP file download. Enhanced downloads are available through the Globus GridFTP "fire and forget" data transfer service, which provides an efficient, reliable, and preferred alternative to traditional HTTP-based methods. For those that favor interoperable access using compatible tools, the Unidata THREDDS Data server provides remote access to complete reanalysis collections by virtual dataset aggregation "files". Finally, users can request data subsets and format conversions to be prepared for them through web interface form requests or web service API batch requests. This approach uses NCAR HPC and central file systems to effectively prepare products from the high-resolution and very large reanalyses archives. The presentation will include a detailed inventory of all RDA reanalysis dataset collection holdings, and highlight access capabilities to these collections through use case examples.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

NASA Astrophysics Data System (ADS)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro Benson; Sharma, Ashish; Evans, Jason Peter; Mehrotra, Rajeshwar; Rocheta, Eytan

2017-08-01

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Large differences in reanalyses of diabatic heating in the tropical upper troposphere and lower stratosphere

NASA Astrophysics Data System (ADS)

Wright, J. S.; Fueglistaler, S.

2013-09-01

We present the time mean heat budgets of the tropical upper troposphere (UT) and lower stratosphere (LS) as simulated by five reanalysis models: the Modern-Era Retrospective Analysis for Research and Applications (MERRA), European Reanalysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), Japanese 25-yr Reanalysis and Japan Meteorological Agency Climate Data Assimilation System (JRA-25/JCDAS), and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1. The simulated diabatic heat budget in the tropical UTLS differs significantly from model to model, with substantial implications for representations of transport and mixing. Large differences are apparent both in the net heat budget and in all comparable individual components, including latent heating, heating due to radiative transfer, and heating due to parameterised vertical mixing. We describe and discuss the most pronounced differences. Discrepancies in latent heating reflect continuing difficulties in representing moist convection in models. Although these discrepancies may be expected, their magnitude is still disturbing. We pay particular attention to discrepancies in radiative heating (which may be surprising given the strength of observational constraints on temperature and tropospheric water vapour) and discrepancies in heating due to turbulent mixing (which have received comparatively little attention). The largest differences in radiative heating in the tropical UTLS are attributable to differences in cloud radiative heating, but important systematic differences are present even in the absence of clouds. Local maxima in heating and cooling due to parameterised turbulent mixing occur in the vicinity of the tropical tropopause.

Decadal Prediction Skill in the GEOS-5 Forecast System

NASA Technical Reports Server (NTRS)

Ham, Yoo-Geun; Rienecker, Michael M.; Suarez, M.; Vikhliaev, Yury V.; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

2012-01-01

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office?s GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). The hindcasts are initialized every December from 1959 to 2010 following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from the atmospheric reanalysis (MERRA, the Modern-Era Retrospective Analysis for Research and Applications) generated using the GEOS-5 atmospheric model. The forecast skill of a three-member-ensemble mean is compared to that of an experiment without initialization but forced with observed CO2. The results show that initialization acts to increase the forecast skill of Northern Atlantic SST compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. The annual-mean Atlantic Meridional Overturning Circulation (AMOC) index is predictable up to a 5-year lead time, consistent with the predictable signal in upper ocean heat content over the Northern Atlantic. While the skill measured by Mean Squared Skill Score (MSSS) shows 50% improvement up to 10-year lead forecast over the subtropical and mid-latitude Atlantic, however, prediction skill is relatively low in the subpolar gyre, due in part to the fact that the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region appears to be unrealistic. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Predictability and prediction of tropical cyclones on daily to interannual time scales

NASA Astrophysics Data System (ADS)

Belanger, James Ian

The spatial and temporal complexity of tropical cyclones (TCs) raises a number of scientific questions regarding their genesis, movement, intensification, and variability. In this dissertation, the principal goal is to determine the current state of predictability for each of these processes using global numerical prediction systems. The predictability findings are then used in conjunction with several new statistical calibration techniques to develop a proof-of-concept, operational forecast system for North Atlantic TCs on daily to intraseasonal time scales. To quantify the current extent of tropical cyclone predictability, we assess probabilistic forecasts from the most advanced global numerical weather prediction system to date, the ECMWF Variable Resolution Ensemble Prediction System (VarEPS; Hamill et al. 2008, Hagedorn et al. 2012). Using a new false alarm clustering technique to maximize the utility of the VarEPS, the ensemble system is shown to provide well-calibrated probabilistic forecasts for TC genesis through a lead-time of one week and pregenesis track forecasts with similar skill compared to the VarEPS's postgenesis track forecasts. These findings provide evidence that skillful real-time TC genesis predictions may be made in the North Indian Ocean—a region that even today has limited forecast warning windows for TCs relative to other ocean basins. To quantify the predictability of TCs on intraseasonal time scales, forecasts from the ECMWF Monthly Forecast System (ECMFS) are examined for the North Atlantic Ocean. From this assessment, dynamically based forecasts from the ECMFS provide forecast skill exceeding climatology out to weeks three and four for portions of the southern Gulf of Mexico, western Caribbean and the Main Development Region. Forecast skill in these regions is traced to the model's ability to capture correctly the variability in deep-layer vertical wind shear as well as the relative frequency of easterly waves moving through these regions. Following the TC predictability studies, a proof-of-concept operational forecast system for North Atlantic TCs is presented for daily to intraseasonal time scales. Findings from the predictability studies are used in conjunction with recently developed forecast calibration techniques to render the VarEPS and ECMFS forecasts more useful in an operational setting. The proposed combination of bias-calibrated regional probabilistic forecast guidance along with objectively-defined measures of confidence is a new way of providing TC forecasts on intraseasonal time scales. On interannual time scales, the predictability of TCs is examined by considering their relationship with tropical Atlantic easterly waves. First, a set of easterly wave climatologies for the Climate Forecast System-Reanalysis, ERA-Interim, ERA-40, and NCEP/NCAR Reanalysis are developed using a new easterly wave tracking algorithm based on 700 hPa curvature relative vorticity anomalies. From the reanalysis-derived easterly wave climatologies, a moderately positive and statistically significant relationship is seen with tropical Atlantic TCs, suggesting that approximately 20-30% of the total variance in the number of TCs on interannual time scales may be explained by the frequency of easterly waves. In relation to large-scale climate modes, the Atlantic Multidecadal Oscillation (AMO) and Atlantic Meridional Mode (AMM) exhibit the strongest positive covariability with Atlantic easterly wave frequency. Besides changes in the number of easterly waves, the intensification efficiency of easterly waves, which is the percentage of waves that induce North Atlantic TC formation, has also been evaluated. These findings offer a plausible physical explanation for the recent increase in the number of NATL TCs, as it has been concomitant with an increasing trend in both the number of tropical Atlantic easterly waves and intensification efficiency. In addition, the easterly wave-tropical cyclone pathway is likely an important mechanism governing how the AMO and AMM modulate North Atlantic TC frequency—more so than previous thought (e.g., Thorncroft and Hodges 2001, Hopsch et al. 2007, Kossin and Vimont 2007). The last component of this dissertation examines how the historical variability in U.S. landfalling TCs has impacted the annual TC tornado record. To reconcile the inhomogeneous, historical tornado record, two statistical tornado models, developed from a set of a priori predictors for TC tornado formation, are used to reconstruct the TC tornado climatology. Based on the evaluation period during the most reliable portion of the TC tornado record, these models possess moderate skill in forecasting the magnitude of a tornado outbreak from a Gulf landfalling TC and have high skill in forecasting the annual number of TC tornadoes. While the synthetic TC tornado record also reflects decadal scale variations in association with the AMO, a comparison of the current warm phase of the AMO with the previous warm phase period shows that the median number of tornadoes per Gulf TC landfall has significantly increased. This change likely reflects the increase in median TC size (by 35%) of Gulf landfalling TCs along with an increased frequency of large TCs at landfall.

Precipitation and floodiness: forecasts of flood hazard at the regional scale

NASA Astrophysics Data System (ADS)

Stephens, Liz; Day, Jonny; Pappenberger, Florian; Cloke, Hannah

2016-04-01

In 2008, a seasonal forecast of an increased likelihood of above-normal rainfall in West Africa led the Red Cross to take early humanitarian action (such as prepositioning of relief items) on the basis that this forecast implied heightened flood risk. However, there are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard, so in this presentation we use a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010) to quantify this non-linearity. Using these data, we introduce the concept of floodiness to measure the incidence of floods over a large area, and quantify the link between monthly precipitation, river discharge and floodiness anomalies. Our analysis shows that floodiness is not well correlated with precipitation, demonstrating the problem of using seasonal precipitation forecasts as a proxy for forecasting flood hazard. This analysis demonstrates the value of developing hydrometeorological forecasts of floodiness for decision-makers. As a result, we are now working with the European Centre for Medium-Range Weather Forecasts and the Joint Research Centre, as partners of the operational Global Flood Awareness System (GloFAS), to implement floodiness forecasts in real-time.

Using Reanalysis to Provide Circulation Context for Ice Cores Recovered from Mt. Hunter Plateau in Denali National Park

NASA Astrophysics Data System (ADS)

Osterberg, E. C.; Birkel, S. D.; Kreutz, K. J.; Wake, C. P.; Campbell, S. W.; Winski, D.

2015-12-01

Researchers from the University of Maine, University of New Hampshire, and Dartmouth College supported by NSF recently recovered two ice cores from the Mt. Hunter Plateau in the Alaska Range of Denali National Park. Ongoing analyses of snow accumulation, snowmelt, stable isotopes, and chemistry within the core are providing proxy information for ~1000 years of regional climate variability. Broader context to link circulation across the North Pacific and western North America can be obtained by using climate reanalysis. In this vein, we are using monthly, daily, and sub-daily meteorological fields from the NCEP Climate Forecasting System Reanalysis (CFSR) to characterize large-scale circulation associated with notable events in the ice core record onward from 1979. One goal is to assess the relationship between annual snow accumulation spikes and storm frequency and magnitude. A second goal is to relate these observations to events during the Little Ice Age and Medieval Warm Period. Work is in progress, and results will be presented at the fall meeting.

Forecasting the magnitude and onset of El Niño based on climate network

NASA Astrophysics Data System (ADS)

Meng, Jun; Fan, Jingfang; Ashkenazy, Yosef; Bunde, Armin; Havlin, Shlomo

2018-04-01

El Niño is probably the most influential climate phenomenon on inter-annual time scales. It affects the global climate system and is associated with natural disasters; it has serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Niño are still not accurate enough, at least more than half a year ahead. Here, we introduce a new forecasting index based on climate network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the Niño 3.4 region. We find that significant upward trends in our index forecast the onset of El Niño approximately 1 year ahead, and the highest peak since the end of last El Niño in our index forecasts the magnitude of the following event. We study the forecasting capability of the proposed index on several datasets, including, ERA-Interim, NCEP Reanalysis I, PCMDI-AMIP 1.1.3 and ERSST.v5.

Attributing Predictable Signals at Subseasonal Timescales

NASA Astrophysics Data System (ADS)

Shelly, A.; Norton, W.; Rowlands, D.; Beech-Brandt, J.

2016-12-01

Subseasonal forecasts offer significant economic value in the management of energy infrastructure and through the associated financial markets. Models are now accurate enough to provide, for some occasions, good forecasts in the subseasonal range. However, it is often not clear what the drivers of these subseasonal signals are and if the forecasts could be more accurate with better representation of physical processes. Also what are the limits of predictability in the subseasonal range? To address these questions, we have run the ECMWF monthly forecast system over the 2015/16 winter with a set of 6 week ensemble integrations initialised every week over the period. In these experiments, we have relaxed the band 15N to 15S to reanalysis fields. Hence, we have a set of forecasts where the tropics is constrained to actual events and we can analyse the changes in predictability in middle latitudes - in particular in regions of high energy consumption like North America and Europe. Not surprisingly, the forecast of some periods are significantly improved while others show no improvement. We discuss events/patterns that have extended range predictability and also the tropical forecast errors which prevent the potential predictability in middle latitudes from being realised.

Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa

NASA Astrophysics Data System (ADS)

Jones, A. E.; Morse, A. P.

2012-12-01

This study examines the performance of malaria-relevant climate variables from the ENSEMBLES seasonal ensemble re-forecasts for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall forecasts into simulated malaria incidence and verifying these forecasts against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of forecast skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the forecast period and interannual variability in simulated malaria is closely linked to variability in temperature, and northern Nigeria/southern Niger, where simulated malaria variability is strongly associated with rainfall variability during the peak rain months.

Evaluation of a new microphysical aerosol module in the ECMWF Integrated Forecasting System

NASA Astrophysics Data System (ADS)

Woodhouse, Matthew; Mann, Graham; Carslaw, Ken; Morcrette, Jean-Jacques; Schulz, Michael; Kinne, Stefan; Boucher, Olivier

2013-04-01

The Monitoring Atmospheric Composition and Climate II (MACC-II) project will provide a system for monitoring and predicting atmospheric composition. As part of the first phase of MACC, the GLOMAP-mode microphysical aerosol scheme (Mann et al., 2010, GMD) was incorporated within the ECMWF Integrated Forecasting System (IFS). The two-moment modal GLOMAP-mode scheme includes new particle formation, condensation, coagulation, cloud-processing, and wet and dry deposition. GLOMAP-mode is already incorporated as a module within the TOMCAT chemistry transport model and within the UK Met Office HadGEM3 general circulation model. The microphysical, process-based GLOMAP-mode scheme allows an improved representation of aerosol size and composition and can simulate aerosol evolution in the troposphere and stratosphere. The new aerosol forecasting and re-analysis system (known as IFS-GLOMAP) will also provide improved boundary conditions for regional air quality forecasts, and will benefit from assimilation of observed aerosol optical depths in near real time. Presented here is an evaluation of the performance of the IFS-GLOMAP system in comparison to in situ aerosol mass and number measurements, and remotely-sensed aerosol optical depth measurements. Future development will provide a fully-coupled chemistry-aerosol scheme, and the capability to resolve nitrate aerosol.

Accuracy and precision of polar lower stratospheric temperatures from reanalyses evaluated from A-Train CALIOP and MLS, COSMIC GPS RO, and the equilibrium thermodynamics of supercooled ternary solutions and ice clouds

NASA Astrophysics Data System (ADS)

Lambert, Alyn; Santee, Michelle L.

2018-02-01

We investigate the accuracy and precision of polar lower stratospheric temperatures (100-10 hPa during 2008-2013) reported in several contemporary reanalysis datasets comprising two versions of the Modern-Era Retrospective analysis for Research and Applications (MERRA and MERRA-2), the Japanese 55-year Reanalysis (JRA-55), the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-I), and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (NCEP-CFSR). We also include the Goddard Earth Observing System model version 5.9.1 near-real-time analysis (GEOS-5.9.1). Comparisons of these datasets are made with respect to retrieved temperatures from the Aura Microwave Limb Sounder (MLS), Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Positioning System (GPS) radio occultation (RO) temperatures, and independent absolute temperature references defined by the equilibrium thermodynamics of supercooled ternary solutions (STSs) and ice clouds. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations of polar stratospheric clouds are used to determine the cloud particle types within the Aura MLS geometric field of view. The thermodynamic calculations for STS and the ice frost point use the colocated MLS gas-phase measurements of HNO3 and H2O. The estimated bias and precision for the STS temperature reference, over the 68 to 21 hPa pressure range, are 0.6-1.5 and 0.3-0.6 K, respectively; for the ice temperature reference, they are 0.4 and 0.3 K, respectively. These uncertainties are smaller than those estimated for the retrieved MLS temperatures and also comparable to GPS RO uncertainties (bias < 0.2 K, precision > 0.7 K) in the same pressure range. We examine a case study of the time-varying temperature structure associated with layered ice clouds formed by orographic gravity waves forced by flow over the Palmer Peninsula and compare how the wave amplitudes are reproduced by each reanalysis dataset. We find that the spatial and temporal distribution of temperatures below the ice frost point, and hence the potential to form ice polar stratospheric clouds (PSCs) in model studies driven by the reanalyses, varies significantly because of the underlying differences in the representation of mountain wave activity. High-accuracy COSMIC temperatures are used as a common reference to intercompare the reanalysis temperatures. Over the 68-21 hPa pressure range, the biases of the reanalyses with respect to COSMIC temperatures for both polar regions fall within the narrow range of -0.6 K to +0.5 K. GEOS-5.9.1, MERRA, MERRA-2, and JRA-55 have predominantly cold biases, whereas ERA-I has a predominantly warm bias. NCEP-CFSR has a warm bias in the Arctic but becomes substantially colder in the Antarctic. Reanalysis temperatures are also compared with the PSC reference temperatures. Over the 68-21 hPa pressure range, the reanalysis temperature biases are in the range -1.6 to -0.3 K with standard deviations ˜ 0.6 K for the CALIOP STS reference, and in the range -0.9 to +0.1 K with standard deviations ˜ 0.7 K for the CALIOP ice reference. Comparisons of MLS temperatures with the PSC reference temperatures reveal vertical oscillations in the MLS temperatures and a significant low bias in MLS temperatures of up to 3 K.

Steps towards a consistent Climate Forecast System Reanalysis wave hindcast (1979-2016)

NASA Astrophysics Data System (ADS)

Stopa, Justin E.; Ardhuin, Fabrice; Huchet, Marion; Accensi, Mickael

2017-04-01

Surface gravity waves are being increasingly recognized as playing an important role within the climate system. Wave hindcasts and reanalysis products of long time series (>30 years) have been instrumental in understanding and describing the wave climate for the past several decades and have allowed a better understanding of extreme waves and inter-annual variability. Wave hindcasts have the advantage of covering the oceans in higher space-time resolution than possible with conventional observations from satellites and buoys. Wave reanalysis systems like ECWMF's ERA-Interim directly included a wave model that is coupled to the ocean and atmosphere, otherwise reanalysis wind fields are used to drive a wave model to reproduce the wave field in long time series. The ERA Interim dataset is consistent in time, but cannot adequately resolve extreme waves. On the other hand, the NCEP Climate Forecast System (CFSR) wind field better resolves the extreme wind speeds, but suffers from discontinuous features in time which are due to the quantity and quality of the remote sensing data incorporated into the product. Therefore, a consistent hindcast that resolves the extreme waves still alludes us limiting our understanding of the wave climate. In this study, we systematically correct the CFSR wind field to reproduce a homogeneous wave field in time. To verify the homogeneity of our hindcast we compute error metrics on a monthly basis using the observations from a merged altimeter wave database which has been calibrated and quality controlled from 1985-2016. Before 1985 only few wave observations exist and are limited to a select number of wave buoys mostly in the North Hemisphere. Therefore we supplement our wave observations with seismic data which responds to nonlinear wave interactions created by opposing waves with nearly equal wavenumbers. Within the CFSR wave hindcast, we find both spatial and temporal discontinuities in the error metrics. The Southern Hemisphere often has wind speed biases larger than the Northern Hemisphere and we propose a simple correction to reduce these features by applying a taper shaped by a half-Hanning window. The discontinuous features in time are corrected by scaling the entire wind field by percentages ranging typically ranging from 1-3%. Our analysis is performed on monthly time series and we expect the monthly statistics to be more adequate for climate studies.

Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

NASA Astrophysics Data System (ADS)

Pavlovic, R.; Menard, S.; Chen, J.; Anselmo, D.; Paul-Andre, B.; Gravel, S.; Moran, M. D.; Davignon, D.

2013-12-01

An emissions processing system has been developed to incorporate near-real-time emissions from wildfires and large prescribed burns into Environment Canada's real-time GEM-MACH air quality (AQ) forecast system. Since the GEM-MACH forecast domain covers Canada and most of the USA, including Alaska, fire location information is needed for both of these large countries. Near-real-time satellite data are obtained and processed separately for the two countries for organizational reasons. Fire location and fuel consumption data for Canada are provided by the Canadian Forest Service's Canadian Wild Fire Information System (CWFIS) while fire location and emissions data for the U.S. are provided by the SMARTFIRE (Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation) system via the on-line BlueSky Gateway. During AQ model runs, emissions from individual fire sources are injected into elevated model layers based on plume-rise calculations and then transport and chemistry calculations are performed. This 'on the fly' approach to the insertion of emissions provides greater flexibility since on-line meteorology is used and reduces computational overhead in emission pre-processing. An experimental wildfire version of GEM-MACH was run in real-time mode for the summers of 2012 and 2013. 48-hour forecasts were generated every 12 hours (at 00 and 12 UTC). Noticeable improvements in the AQ forecasts for PM2.5 were seen in numerous regions where fire activity was high. Case studies evaluating model performance for specific regions, computed objective scores, and subjective evaluations by AQ forecasters will be included in this presentation. Using the lessons learned from the last two summers, Environment Canada will continue to work towards the goal of incorporating near-real-time intermittent wildfire emissions within the operational air quality forecast system.

Seasonal Predictions with the GEOS GCM

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Chang, Yehui; Suarez, Max

1999-01-01

A number of ensembles of seasonal forecasts have recently been completed as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus is on the extratropical response of the atmosphere to observed Surface Sea Temperature (SST) anomalies during boreal winter. The prediction experiments consist of nine forecasts starting from slightly different initial conditions for each year of the 15 year period 1981-95, employing version 2 of the Goddard Earth Observing System (GEOS) atmospheric Global Circulation Models (GCM). The initial conditions are obtained from the NASA GEOS-1 reanalysis data. Comparisons with a companion set of six long-term simulations with observed SST (starting in 1978, so they have no memory of the initial conditions for the periods of interest) are used to assess the relative contributions of the initial conditions and SST anomalies to forecast skill ranging from daily to seasonal time scales. The ensembles are used to isolate the signal, and to assess the nature of the inherent variability (noise) of the forecasts.

Evaluation of the CFSv2 CMIP5 decadal predictions

NASA Astrophysics Data System (ADS)

Bombardi, Rodrigo J.; Zhu, Jieshun; Marx, Lawrence; Huang, Bohua; Chen, Hua; Lu, Jian; Krishnamurthy, Lakshmi; Krishnamurthy, V.; Colfescu, Ioana; Kinter, James L.; Kumar, Arun; Hu, Zeng-Zhen; Moorthi, Shrinivas; Tripp, Patrick; Wu, Xingren; Schneider, Edwin K.

2015-01-01

Retrospective decadal forecasts were undertaken using the Climate Forecast System version 2 (CFSv2) as part of Coupled Model Intercomparison Project 5. Decadal forecasts were performed separately by the National Center for Environmental Prediction (NCEP) and by the Center for Ocean-Land-Atmosphere Studies (COLA), with the centers using two different analyses for the ocean initial conditions the NCEP Climate Forecast System Reanalysis (CFSR) and the NEMOVAR-COMBINE analysis. COLA also examined the sensitivity to the inclusion of forcing by specified volcanic aerosols. Biases in the CFSv2 for both sets of initial conditions include cold midlatitude sea surface temperatures, and rapid melting of sea ice associated with warm polar oceans. Forecasts from the NEMOVAR-COMBINE analysis showed strong weakening of the Atlantic Meridional Overturning Circulation (AMOC), eventually approaching the weaker AMOC associated with CFSR. The decadal forecasts showed high predictive skill over the Indian, the western Pacific, and the Atlantic Oceans and low skill over the central and eastern Pacific. The volcanic forcing shows only small regional differences in predictability of surface temperature at 2m (T2m) in comparison to forecasts without volcanic forcing, especially over the Indian Ocean. An ocean heat content (OHC) budget analysis showed that the OHC has substantial memory, indicating potential for the decadal predictability of T2m; however, the model has a systematic drift in global mean OHC. The results suggest that the reduction of model biases may be the most productive path towards improving the model's decadal forecasts.

Severe Weather Environments in Atmospheric Reanalyses

NASA Astrophysics Data System (ADS)

King, A. T.; Kennedy, A. D.

2017-12-01

Atmospheric reanalyses combine historical observation data using a fixed assimilation scheme to achieve a dynamically coherent representation of the atmosphere. How well these reanalyses represent severe weather environments via proxies is poorly defined. To quantify the performance of reanalyses, a database of proximity soundings near severe storms from the Rapid Update Cycle 2 (RUC-2) model will be compared to a suite of reanalyses including: North American Reanalysis (NARR), European Interim Reanalysis (ERA-Interim), 2nd Modern-Era Retrospective Reanalysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55), 20th Century Reanalysis (20CR), and Climate Forecast System Reanalysis (CFSR). A variety of severe weather parameters will be calculated from these soundings including: convective available potential energy (CAPE), storm relative helicity (SRH), supercell composite parameter (SCP), and significant tornado parameter (STP). These soundings will be generated using the SHARPpy python module, which is an open source tool used to calculate severe weather parameters. Preliminary results indicate that the NARR and JRA55 are significantly more skilled at producing accurate severe weather environments than the other reanalyses. The primary difference between these two reanalyses and the remaining reanalyses is a significant negative bias for thermodynamic parameters. To facilitate climatological studies, the scope of work will be expanded to compute these parameters for the entire domain and duration of select renalyses. Preliminary results from this effort will be presented and compared to observations at select locations. This dataset will be made pubically available to the larger scientific community, and details of this product will be provided.

Modulation of Soil Initial State on WRF Model Performance Over China

NASA Astrophysics Data System (ADS)

Xue, Haile; Jin, Qinjian; Yi, Bingqi; Mullendore, Gretchen L.; Zheng, Xiaohui; Jin, Hongchun

2017-11-01

The soil state (e.g., temperature and moisture) in a mesoscale numerical prediction model is typically initialized by reanalysis or analysis data that may be subject to large bias. Such bias may lead to unrealistic land-atmosphere interactions. This study shows that the Climate Forecast System Reanalysis (CFSR) dramatically underestimates soil temperature and overestimates soil moisture over most parts of China in the first (0-10 cm) and second (10-25 cm) soil layers compared to in situ observations in July 2013. A correction based on the global optimal dual kriging is employed to correct CFSR bias in soil temperature and moisture using in situ observations. To investigate the impacts of the corrected soil state on model forecasts, two numerical model simulations—a control run with CFSR soil state and a disturbed run with the corrected soil state—were conducted using the Weather Research and Forecasting model. All the simulations are initiated 4 times per day and run 48 h. Model results show that the corrected soil state, for example, warmer and drier surface over the most parts of China, can enhance evaporation over wet regions, which changes the overlying atmospheric temperature and moisture. The changes of the lifting condensation level, level of free convection, and water transport due to corrected soil state favor precipitation over wet regions, while prohibiting precipitation over dry regions. Moreover, diagnoses indicate that the remote moisture flux convergence plays a dominant role in the precipitation changes over the wet regions.

Near-surface wind speed statistical distribution: comparison between ECMWF System 4 and ERA-Interim

NASA Astrophysics Data System (ADS)

Marcos, Raül; Gonzalez-Reviriego, Nube; Torralba, Verónica; Cortesi, Nicola; Young, Doo; Doblas-Reyes, Francisco J.

2017-04-01

In the framework of seasonal forecast verification, knowing whether the characteristics of the climatological wind speed distribution, simulated by the forecasting systems, are similar to the observed ones is essential to guide the subsequent process of bias adjustment. To bring some light about this topic, this work assesses the properties of the statistical distributions of 10m wind speed from both ERA-Interim reanalysis and seasonal forecasts of ECMWF system 4. The 10m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis) together with the coefficient of variation and goodness of fit Shapiro-Wilks test, allowing the identification of regions with higher wind variability and non-Gaussian behaviour at monthly time-scales. Also, the comparison of the predicted and observed 10m wind speed distributions has been measured considering both inter-annual and intra-seasonal variability. Such a comparison is important in both climate research and climate services communities because it provides useful climate information for decision-making processes and wind industry applications.

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.

Cloud-Enabled Climate Analytics-as-a-Service using Reanalysis data: A case study.

NASA Astrophysics Data System (ADS)

Nadeau, D.; Duffy, D.; Schnase, J. L.; McInerney, M.; Tamkin, G.; Potter, G. L.; Thompson, J. H.

2014-12-01

The NASA Center for Climate Simulation (NCCS) maintains advanced data capabilities and facilities that allow researchers to access the enormous volume of data generated by weather and climate models. The NASA Climate Model Data Service (CDS) and the NCCS are merging their efforts to provide Climate Analytics-as-a-Service for the comparative study of the major reanalysis projects: ECMWF ERA-Interim, NASA/GMAO MERRA, NOAA/NCEP CFSR, NOAA/ESRL 20CR, JMA JRA25, and JRA55. These reanalyses have been repackaged to netCDF4 file format following the CMIP5 Climate and Forecast (CF) metadata convention prior to be sequenced into the Hadoop Distributed File System ( HDFS ). A small set of operations that represent a common starting point in many analysis workflows was then created: min, max, sum, count, variance and average. In this example, Reanalysis data exploration was performed with the use of Hadoop MapReduce and accessibility was achieved using the Climate Data Service(CDS) application programming interface (API) created at NCCS. This API provides a uniform treatment of large amount of data. In this case study, we have limited our exploration to 2 variables, temperature and precipitation, using 3 operations, min, max and avg and using 30-year of Reanalysis data for 3 regions of the world: global, polar, subtropical.

Sensitivity of a numerical wave model on wind re-analysis datasets

NASA Astrophysics Data System (ADS)

Lavidas, George; Venugopal, Vengatesan; Friedrich, Daniel

2017-03-01

Wind is the dominant process for wave generation. Detailed evaluation of metocean conditions strengthens our understanding of issues concerning potential offshore applications. However, the scarcity of buoys and high cost of monitoring systems pose a barrier to properly defining offshore conditions. Through use of numerical wave models, metocean conditions can be hindcasted and forecasted providing reliable characterisations. This study reports the sensitivity of wind inputs on a numerical wave model for the Scottish region. Two re-analysis wind datasets with different spatio-temporal characteristics are used, the ERA-Interim Re-Analysis and the CFSR-NCEP Re-Analysis dataset. Different wind products alter results, affecting the accuracy obtained. The scope of this study is to assess different available wind databases and provide information concerning the most appropriate wind dataset for the specific region, based on temporal, spatial and geographic terms for wave modelling and offshore applications. Both wind input datasets delivered results from the numerical wave model with good correlation. Wave results by the 1-h dataset have higher peaks and lower biases, in expense of a high scatter index. On the other hand, the 6-h dataset has lower scatter but higher biases. The study shows how wind dataset affects the numerical wave modelling performance, and that depending on location and study needs, different wind inputs should be considered.

The Influence of the North Atlantic Oscillation on Tropospheric Distributions of Ozone and Carbon Monoxide.

NASA Astrophysics Data System (ADS)

Knowland, K. E.; Doherty, R. M.; Hodges, K.

2015-12-01

The influence of the North Atlantic Oscillation (NAO) on the tropospheric distributions of ozone (O3) and carbon monoxide (CO) has been quantified. The Monitoring Atmospheric Composition and Climate (MACC) Reanalysis, a combined meteorology and composition dataset for the period 2003-2012 (Innes et al., 2013), is used to investigate the composition of the troposphere and lower stratosphere in relation to the location of the storm track as well as other meteorological parameters over the North Atlantic associated with the different NAO phases. Cyclone tracks in the MACC Reanalysis compare well to the cyclone tracks in the widely-used ERA-Interim Reanalysis for the same 10-year period (cyclone tracking performed using the tracking algorithm of Hodges (1995, 1999)), as both are based on the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS). A seasonal analysis is performed whereby the MACC reanalysis meteorological fields, O3 and CO mixing ratios are weighted by the monthly NAO index values. The location of the main storm track, which tilts towards high latitudes (toward the Arctic) during positive NAO phases to a more zonal location in the mid-latitudes (toward Europe) during negative NAO phases, impacts the location of both horizontal and vertical transport across the North Atlantic and into the Arctic. During positive NAO seasons, the persistence of cyclones over the North Atlantic coupled with a stronger Azores High promotes strong horizontal transport across the North Atlantic throughout the troposphere. In all seasons, significantly more intense cyclones occur at higher latitudes (north of ~50°C) during the positive phase of the NAO and in the southern mid-latitudes during the negative NAO phase. This impacts the location of stratospheric intrusions within the descending dry airstream behind the associated cold front of the extratropical cyclone and the venting of low-level pollution up into the free troposphere within the warm conveyor belt airstream which rises ahead of the cold front.

Simulating seasonal tropical cyclone intensities at landfall along the South China coast

NASA Astrophysics Data System (ADS)

Lok, Charlie C. F.; Chan, Johnny C. L.

2018-04-01

A numerical method is developed using a regional climate model (RegCM3) and the Weather Forecast and Research (WRF) model to predict seasonal tropical cyclone (TC) intensities at landfall for the South China region. In designing the model system, three sensitivity tests have been performed to identify the optimal choice of the RegCM3 model domain, WRF horizontal resolution and WRF physics packages. Driven from the National Centers for Environmental Prediction Climate Forecast System Reanalysis dataset, the model system can produce a reasonable distribution of TC intensities at landfall on a seasonal scale. Analyses of the model output suggest that the strength and extent of the subtropical ridge in the East China Sea are crucial to simulating TC landfalls in the Guangdong and Hainan provinces. This study demonstrates the potential for predicting TC intensities at landfall on a seasonal basis as well as projecting future climate changes using numerical models.

Decadal Prediction Skill in the GEOS-5 Forecast System

NASA Technical Reports Server (NTRS)

Ham, Yoo-Geun; Rienecker, Michele M.; Suarez, Max J.; Vikhliaev, Yury; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

2013-01-01

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling

NASA Astrophysics Data System (ADS)

Dukhovskoy, Dmitry S.; Bourassa, Mark A.; Petersen, Gudrún Nína; Steffen, John

2017-03-01

Ocean surface vector wind fields from reanalysis data sets and scatterometer-derived gridded products are analyzed over the Nordic Seas and the northern North Atlantic for the time period from 2000 to 2009. The data sets include the National Center for Environmental Prediction Reanalysis 2 (NCEPR2), Climate Forecast System Reanalysis (CFSR), Arctic System Reanalysis (ASR), Cross-Calibrated Multiplatform (CCMP) wind product version 1.1 and recently released version 2.0, and QuikSCAT. The goal of the study is to assess discrepancies across the wind vector fields in the data sets and demonstrate possible implications of these differences for ocean modeling. Large-scale and mesoscale characteristics of winds are compared at interannual, seasonal, and synoptic timescales. A cyclone tracking methodology is developed and applied to the wind fields to compare cyclone characteristics in the data sets. Additionally, the winds are evaluated against observations collected from meteorological buoys deployed in the Iceland and Irminger Seas. The agreement among the wind fields is better for longer time and larger spatial scales. The discrepancies are clearly apparent for synoptic timescales and mesoscales. CCMP, ASR, and CFSR show the closest overall agreement with each other. Substantial biases are found in the NCEPR2 winds. Numerical sensitivity experiments are conducted with a coupled ice-ocean model forced by different wind fields. The experiments demonstrate differences in the net surface heat fluxes during storms. In the experiment forced by NCEPR2 winds, there are discrepancies in the large-scale wind-driven ocean dynamics compared to the other experiments.

The Mediterranean interannual variability in MEDRYS, a Mediterranean Sea reanalysis over 1992-2013

NASA Astrophysics Data System (ADS)

Beuvier, Jonathan; Hamon, Mathieu; Lellouche, Jean-Michel; Greiner, Eric; Alias, Antoinette; Arsouze, Thomas; Benkiran, Mounir; Béranger, Karine; Drillet, Yann; Sevault, Florence; Somot, Samuel

2015-04-01

The French research community on the Mediterranean Sea and the French operational ocean forecasting center Mercator Océan are gathering their skills and expertises in physical oceanography, ocean modelling, atmospheric forcings and data assimilation, to carry out a MEDiterranean Sea ReanalYsiS (MEDRYS) at high resolution for the period 1992-2013. The reanalysis is used to have a realistic description of the ocean state over the recent decades and it will help to understand the long-term water cycle over the Mediterranean basin in terms of variability and trends, contributing thus to the HyMeX international program. The ocean model used is NEMOMED12 [Lebeaupin Brossier et al., 2011, Oc. Mod., 2012, Oc. Mod.; Beuvier et al., 2012a, JGR, 2012b, Mercator Newsl.], a Mediterranean configuration of NEMO [Madec and the NEMO Team, 2008], with a 1/12° (about 7 km) horizontal resolution and 75 vertical z-levels with partial steps. It is forced by the 3-hourly atmospheric fluxes coming from an ALADIN-Climate simulation at 12 km of resolution [Herrmann et al., 2011, NHESS], driven by the ERA-Interim atmospheric reanalysis. The exchanges with the Atlantic Ocean are performed through a buffer zone, with a damping on 3D theta-S and on sea level towards the ORA-S4 oceanic reanalysis [Balmaseda et al., 2012, QJRMS]. This model configuration is used to carry a 34-year free simulation over the period 1979-2013. This free simulation is the initial state of the reanalysis in October 1992. It is also used to compute anomalies from which the data assimilation scheme derives required characteristic covariances of the ocean model. MEDRYS1 uses the current Mercator Océan operational data assimilation system [Lellouche et al., 2013, Oc.Sci.]. It uses a reduced order Kalman filter with a 3D multivariate modal decomposition of the forecast error. A 3D-Var scheme corrects biases in temperature and salinity for the slowly evolving large-scale. In addition, some modifications dedicated to the Mediterranean area (more specific Post-Glacial-Rebound corrections, new model-equivalent for the Sea Level Anomaly for example) have been introduced. Temperature and salinity vertical profiles from the newly released CORA4 database, altimeter data and satellite SST and are jointly assimilated. Thus, the reanalysis benefits from the intensive observational field campaigns carried out during the HyMeX Special Observation Periods (SOPs) in fall 2012 and winter 2013 in the north-western Mediterranean Sea. We assess here the ability of a MEDRYS1 to reproduce the general circulation and the water masses in the Mediterranean Sea. We present the misfit between the reanalysis and the assimilated observations, as well as differences between the reanalysis and its twin free simulation. We show diagnostics on the surface circulation variability, heat and salt contents and deep water formation over the whole period of the reanalysis, with also a focus on the impact of the HyMeX data during the SOPs time period.

Tropospheric chemistry in the integrated forecasting system of ECMWF

NASA Astrophysics Data System (ADS)

Flemming, J.; Huijnen, V.; Arteta, J.; Bechtold, P.; Beljaars, A.; Blechschmidt, A.-M.; Josse, B.; Diamantakis, M.; Engelen, R. J.; Gaudel, A.; Inness, A.; Jones, L.; Katragkou, E.; Marecal, V.; Peuch, V.-H.; Richter, A.; Schultz, M. G.; Stein, O.; Tsikerdekis, A.

2014-11-01

A representation of atmospheric chemistry has been included in the Integrated Forecasting System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF). The new chemistry modules complement the aerosol modules of the IFS for atmospheric composition, which is named C-IFS. C-IFS for chemistry supersedes a coupled system, in which the Chemical Transport Model (CTM) Model for OZone and Related chemical Tracers 3 was two-way coupled to the IFS (IFS-MOZART). This paper contains a description of the new on-line implementation, an evaluation with observations and a comparison of the performance of C-IFS with MOZART and with a re-analysis of atmospheric composition produced by IFS-MOZART within the Monitoring Atmospheric Composition and Climate (MACC) project. The chemical mechanism of C-IFS is an extended version of the Carbon Bond 2005 (CB05) chemical mechanism as implemented in the CTM Transport Model 5 (TM5). CB05 describes tropospheric chemistry with 54 species and 126 reactions. Wet deposition and lightning nitrogen monoxide (NO) emissions are modelled in C-IFS using the detailed input of the IFS physics package. A one-year simulation by C-IFS, MOZART and the MACC re-analysis is evaluated against ozonesondes, carbon monoxide (CO) aircraft profiles, European surface observations of ozone (O3), CO, sulphur dioxide (SO2) and nitrogen dioxide (NO2) as well as satellite retrievals of CO, tropospheric NO2 and formaldehyde. Anthropogenic emissions from the MACC/CityZen (MACCity) inventory and biomass burning emissions from the Global Fire Assimilation System (GFAS) data set were used in the simulations by both C-IFS and MOZART. C-IFS (CB05) showed an improved performance with respect to MOZART for CO, upper tropospheric O3, winter time SO2 and was of a similar accuracy for other evaluated species. C-IFS (CB05) is about ten times more computationally efficient than IFS-MOZART.

Tropospheric chemistry in the Integrated Forecasting System of ECMWF

NASA Astrophysics Data System (ADS)

Flemming, J.; Huijnen, V.; Arteta, J.; Bechtold, P.; Beljaars, A.; Blechschmidt, A.-M.; Diamantakis, M.; Engelen, R. J.; Gaudel, A.; Inness, A.; Jones, L.; Josse, B.; Katragkou, E.; Marecal, V.; Peuch, V.-H.; Richter, A.; Schultz, M. G.; Stein, O.; Tsikerdekis, A.

2015-04-01

A representation of atmospheric chemistry has been included in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new chemistry modules complement the aerosol modules of the IFS for atmospheric composition, which is named C-IFS. C-IFS for chemistry supersedes a coupled system in which chemical transport model (CTM) Model for OZone and Related chemical Tracers 3 was two-way coupled to the IFS (IFS-MOZART). This paper contains a description of the new on-line implementation, an evaluation with observations and a comparison of the performance of C-IFS with MOZART and with a re-analysis of atmospheric composition produced by IFS-MOZART within the Monitoring Atmospheric Composition and Climate (MACC) project. The chemical mechanism of C-IFS is an extended version of the Carbon Bond 2005 (CB05) chemical mechanism as implemented in CTM Transport Model 5 (TM5). CB05 describes tropospheric chemistry with 54 species and 126 reactions. Wet deposition and lightning nitrogen monoxide (NO) emissions are modelled in C-IFS using the detailed input of the IFS physics package. A 1 year simulation by C-IFS, MOZART and the MACC re-analysis is evaluated against ozonesondes, carbon monoxide (CO) aircraft profiles, European surface observations of ozone (O3), CO, sulfur dioxide (SO2) and nitrogen dioxide (NO2) as well as satellite retrievals of CO, tropospheric NO2 and formaldehyde. Anthropogenic emissions from the MACC/CityZen (MACCity) inventory and biomass burning emissions from the Global Fire Assimilation System (GFAS) data set were used in the simulations by both C-IFS and MOZART. C-IFS (CB05) showed an improved performance with respect to MOZART for CO, upper tropospheric O3, and wintertime SO2, and was of a similar accuracy for other evaluated species. C-IFS (CB05) is about 10 times more computationally efficient than IFS-MOZART.

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

NASA Astrophysics Data System (ADS)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

Recent Reanalysis Activities at ECMWF: Results from ERA-20C and Plans for ERA5

NASA Astrophysics Data System (ADS)

Dragani, R.; Hersbach, H.; Poli, P.; Pebeuy, C.; Hirahara, S.; Simmons, A.; Dee, D.

2015-12-01

This presentation will provide an overview of the most recent reanalysis activities performed at the European Centre for Medium-Range Weather Forecasts (ECMWF). A pilot reanalysis of the 20th-century (ERA-20C) has recently been completed. Funded through the European FP7 collaborative project ERA-CLIM, ERA-20C is part of a suite of experiments that also includes a model-only integration (ERA-20CM) and a land-surface reanalysis (ERA-20CL). Its data assimilation system is constrained by only surface observations obtained from ISPD (3.2.6) and ICOADS (2.5.1). Surface boundary conditions are provided by the Hadley Centre (HadISST2.1.0.0) and radiative forcing follows CMIP5 recommended data sets. First-guess uncertainty estimates are based on a 10-member ensemble of Data Assimilations, ERA-20C ensemble, run prior to ERA-20C using ten SST and sea-ice realizations from the Hadley Centre. In November 2014, the European Commission entrusted ECMWF to run on its behalf the Copernicus Climate Change Service (C3S) aiming at producing quality-assured information about the past, current and future states of the climate at both European and global scales. Reanalysis will be one of the main components of the C3S portfolio and the first one to be produced is a global modern era reanalysis (ERA5) covering the period from 1979 onwards. Based on a recent version of the ECMWF data assimilation system, ERA5 will replace the widely used ERA-Interim dataset. This new production will benefit from a much improved model, and better characterized and exploited observations compared to its predecessor. The first part of the presentation will focus on the ERA-20C production, provide an overview of its main characteristics and discuss some of the key results from its assessment. The second part of the talk will give an overview of ERA5, and briefly discuss some of its challenges.

On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours reanalysis-run of NAQFC using same-day observed fire emission are being tested. Preliminary results have shown that this procedure greatly improves the PM2.5 predictions at both nearby and downstream areas from fire sources. The 24 hours reanalysis-run is critical and necessary especially during extreme fire events to provide better PM2.5 predictions.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

A Humidity-Driven Prediction System for Influenza Outbreaks

NASA Astrophysics Data System (ADS)

Thrastarson, H. T.; Teixeira, J.

2015-12-01

Recent studies have highlighted the role of absolute (or specific) humidity conditions as a leading explanation for the seasonal behavior of influenza outbreaks in temperate regions. If the timing and intensity of seasonal influenza outbreaks can be forecast, this would be of great value for public health response efforts. We have developed and implemented a SIRS (Susceptible-Infectious-Recovered-Susceptible) type numerical prediction system that is driven by specific humidity to predict influenza outbreaks. For the humidity, we have explored using both satellite data from the AIRS (Atmospheric Infrared Sounder) instrument as well as ERA-Interim re-analysis data. We discuss the development, testing, sensitivities and limitations of the prediction system and show results for influenza outbreaks in the United States during the years 2010-2014 (modeled in retrospect). Comparisons are made with other existing prediction systems and available data for influenza outbreaks from Google Flu Trends and the CDC (Center for Disease Control), and the incorporation of these datasets into the forecasting system is discussed.

Understanding the land-atmospheric interaction in drought forecast from CFSv2 for the 2011 Texas and 2012 Upper Midwest US droughts

NASA Astrophysics Data System (ADS)

Zhang, Y.; Roundy, J. K.; Ek, M. B.; Wood, E. F.

2015-12-01

Prediction and thus preparedness in advance of hydrological extremes, such as drought and flood events, is crucial for proactively reducing their social and economic impacts. In the summers of 2011 Texas, and 2012 the Upper Midwest, experienced intense droughts that affected crops and the food market in the US. It is expected that seasonal forecasts with sufficient skill would reduce the negative impacts through planning and preparation. However, the forecast skill from models such as Climate Forecast System Version 2 (CFSv2) from National Centers for Environmental Prediction (NCEP) is low over the US, especially during the warm season (Jun - Sep), which restricts their practical use for drought prediction. This study analyzes the processes that lead to premature termination of 2011 and 2012 US summer droughts in CFSv2 forecast resulting in its low forecast skill. Using the North American Land Data Assimilation System version 2 (NLDAS2) and Climate Forecast System Reanalysis (CFSR) as references, this study investigates the forecast skills of CFSv2 initialized at 00, 06, 12, 18z from May 15 - 31 (leads out to September) for each event in terms of land-atmosphere interaction, through a recently developed Coupling Drought Index (CDI), which is based on the Convective Triggering Potential-Humidity Index-soil moisture (CTP-HI-SM) classification of four climate regimes: wet coupling, dry coupling, transitional and atmospherically controlled. A recycling model is used to trace the moisture sources in the CFSv2 forecasts of anomalous precipitation, which lead to the breakdown of drought conditions and a lack of drought forecasting skills. This is then compared with tracing the moisture source in CFSR with the same recycling model, which is used as the verification for the same periods. This helps to identify the parameterization that triggered precipitation in CFSv2 during 2011 and 2012 summer in the US thus has the potential to improve the forecast skill of CSFv2.

Developing a high-resolution regional atmospheric reanalysis for Australia

NASA Astrophysics Data System (ADS)

White, Christopher; Fox-Hughes, Paul; Su, Chun-Hsu; Jakob, Dörte; Kociuba, Greg; Eisenberg, Nathan; Steinle, Peter; Harris, Rebecca; Corney, Stuart; Love, Peter; Remenyi, Tomas; Chladil, Mark; Bally, John; Bindoff, Nathan

2017-04-01

A dynamically consistent, long-term atmospheric reanalysis can be used to support high-quality assessments of environmental risk and likelihood of extreme events. Most reanalyses are presently based on coarse-scale global systems that are not suitable for regional assessments in fire risk, water and natural resources, amongst others. The Australian Bureau of Meteorology is currently working to close this gap by producing a high-resolution reanalysis over the Australian and New Zealand region to construct a sequence of atmospheric conditions at sub-hourly intervals over the past 25 years from 1990. The Australia reanalysis consists of a convective-scale analysis nested within a 12 km regional-scale reanalysis, which is bounded by a coarse-scale ERA-Interim reanalysis that provides the required boundary and initial conditions. We use an unchanging atmospheric modelling suite based on the UERRA system used at the UK Met Office and the more recent version of the Bureau of Meteorology's operational numerical prediction model used in ACCESS-R (Australian Community Climate and Earth-System Simulator-Regional system). An advanced (4-dimensional variational) data assimilation scheme is used to optimally combine model physics with multiple observations from aircrafts, sondes, surface observations and satellites to create a best estimate of state of the atmosphere over a 6-hour moving window. This analysis is in turn used to drive a higher-resolution (1.5 km) downscaling model over selected subdomains within Australia, currently eastern New South Wales and Tasmania, with the capability to support this anywhere in the Australia-New Zealand domain. The temporal resolution of the gridded analysis fields for both the regional and higher-resolution subdomains are generally one hour, with many fields such as 10 m winds and 2 m temperatures available every 10 minutes. The reanalysis also produces many other variables that include wind, temperature, moisture, pressure, cloud cover, precipitation, evaporation, soil water, and energy fluxes. In this presentation, we report on the implementation of the Australia regional reanalysis and results from first stages of the project, with a focus on the Tasmanian subdomain. An initial benchmarking 1.5 km data set - referred to as the 'Initial Analysis' - has been constructed over the subdomains consisting of regridded and harmonised analysis and short-term forecast fields from the operational ACCESS-C model using the past 5 years (2011-2015) of archived data. Evaluation of the Initial Analysis against surface observations from automatic weather stations indicate changes in model skills over time that may be attributed to changes in NWP and assimilation systems, and model cycling frequency. Preliminary evaluations of the reanalysis across Tasmania and its inter-comparisons with the Initial Analysis and the ERA-Interim reanalysis products will be presented, including some features across the Tasmanian subdomain such as means and extremes of analysed weather variables. Finally, we describe a number of applications across Tasmania of the reanalysis of immediate interest to meteorologists, fire and landscape managers and other members of the emergency management community, including the use of the data to create post-processed fields such as soil dryness, tornados and fire danger indices for forest fire danger risk assessment, including a climatology of Continuous Haines Index.

Water Cycle Variability over the Global Oceans Estimated Using Homogenized Reanalysis Fluxes

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.

2017-12-01

Establishing consistent records of the global water cycle fluxes and their variations is particularly difficult over oceans where the density of in situ observations varies enormously with time, satellite retrievals of flux processes are sparse, and reanalyses are uncertain. The latter have the positive attribute of assimilating diverse observations to provide boundary fluxes and transports but are hindered by at least two factors: (1) the physical parameterizations are imperfect and, (2) the forcing data availability and quality vary greatly in time and, thus, can induce time-dependent, false signals of climate variability. Here we examine the prospects for homogenization of reanalysis records, that is, identifying and greatly minimizing non-physical signals. Our analysis focuses on the satellite era, 1980 to near present. The strategy involves three atmospheric reanalysis systems: (1) the NASA MERRA-2, (2) the newest reanalysis produced by the Japanese Meteorological Agency, JRA-55, and (3) the European Centre for Medium Range Weather Forecasts 20th Century reanalysis, ERA-20C. MERRA-2 and ERA-20C are also accompanied by 10-member AMIP integrations, and JRA-55 by a reanalysis using only conventional observations, JRA-55C. Differencing these latter integrations from the more comprehensive reanalyses helps provide a clearer picture of the impact of satellite observations by removing the effects of SST forcing. This facilitates the use of principal component analysis as a tool to identify and remove non-physical signals. We then use these homogenized E, P and moisture transports to examine the consistency of diagnostics of thermodynamic and hydrologic scaling, especially the P-E pattern amplification or the "wet-get-wetter, dry-get-drier" response. Prospects for further validation by new turbulent flux retrievals by satellite are discussed.

An Update on the VAMOS Extremes Working Group Activities

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Cavalcanti, Iracema

2011-01-01

We review here the progress of the Variability of the American MOnsoon Systems (VAMOS) extremes working group since it was formed in February of 2010. The goals of the working group are to 1) develop an atlas of warm-season extremes over the Americas, 2) evaluate existing and planned simulations, and 3) suggest new model runs to address mechanisms and predictability of extremes. Substantial progress has been made in the development of an extremes atlas based on gridded observations and several reanalysis products including Modern Era Retrospective-Analysis for Research and Applications (MERRA) and Climate Forecast System Reanalysis (CFSR). The status of the atlas, remaining issues and plans for its expansion to include model data will be discussed. This includes the possibility of adding a companion atlas based on station observations based on the software developed under the World Climate Research Programme (WCRP) Expert Team on Climate Change. Detection and Indices (ETCCDI) activity. We will also review progress on relevant research and plans for the use and validation of the atlas results.

Monitoring and long-term assessment of the Mediterranean Sea physical state

NASA Astrophysics Data System (ADS)

Simoncelli, Simona; Fratianni, Claudia; Clementi, Emanuela; Drudi, Massimiliano; Pistoia, Jenny; Grandi, Alessandro; Del Rosso, Damiano

2017-04-01

The near real time monitoring and long-term assessment of the physical state of the ocean are crucial for the wide CMEMS user community providing a continuous and up to date overview of key indicators computed from operational analysis and reanalysis datasets. This constitutes an operational warning system on particular events, stimulating the research towards a deeper understanding of them and consequently increasing CMEMS products uptake. Ocean Monitoring Indicators (OMIs) of some Essential Ocean Variables have been identified and developed by the Mediterranean Monitoring and Forecasting Centre (MED-MFC) under the umbrella of the CMEMS MYP WG (Multi Year Products Working Group). These OMIs have been operationally implemented starting from the physical reanalysis products and then they have been applied to the operational analyses product. Sea surface temperature, salinity, height as well as heat, water and momentum fluxes at the air-sea interface have been operationally implemented since the reanalysis system development as a real time monitoring of the data production. Their consistency analysis against available observational products or budget values recognized in literature guarantees the high quality of the numerical dataset. The results of the reanalysis validation procedures are yearly published in the QUality Information Document since 2014 available through the CMEMS catalogue (http://marine.copernicus.eu), together with the yearly dataset extension. New OMIs of the winter mixed layer depth, the eddy kinetic energy and the heat content will be presented, in particular we will analyze their time evolution and trends starting from 1987, then we will focus on the recent time period 2013-2016 when reanalysis and analyses datasets overlap to show their consistency beside their different system implementation (i.e. atmospheric forcing, wave coupling, nesting). At the end the focus will be on 2016 sea state and circulation of the Mediterranean Sea and its anomaly with respect to the climatological fields to early detect the 2016 peculiarities.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Seasonal Forecasting of Reservoir Inflow for the Segura River Basin, Spain

NASA Astrophysics Data System (ADS)

de Tomas, Alberto; Hunink, Johannes

2017-04-01

A major threat to the agricultural sector in Europe is an increasing occurrence of low water availability for irrigation, affecting the local and regional food security and economies. Especially in the Mediterranean region, such as in the Segura river basin (Spain), drought epidodes are relatively frequent. Part of the irrigation water demand in this basin is met by a water transfer from the Tagus basin (central Spain), but also in this basin an increasing pressure on the water resources has reduced the water available to be transferred. Currently, Drought Management Plans in these Spanish basins are in place and mitigate the impact of drought periods to some extent. Drought indicators that are derived from the available water in the storage reservoirs impose a set of drought mitigation measures. Decisions on water transfers are dependent on a regression-based time series forecast from the reservoir inflows of the preceding months. This user-forecast has its limitations and can potentially be improved using more advanced techniques. Nowadays, seasonal climate forecasts have shown to have increasing skill for certain areas and for certain applications. So far, such forecasts have not been evaluated in a seasonal hydrologic forecasting system in the Spanish context. The objective of this work is to develop a prototype of a Seasonal Hydrologic Forecasting System and compare this with a reference forecast. The reference forecast in this case is the locally used regression-based forecast. Additionally, hydrological simulations derived from climatological reanalysis (ERA-Interim) are taken as a reference forecast. The Spatial Processes in Hydrology model (SPHY - http://www.sphy.nl/) forced with the ECMWF- SFS4 (15 ensembles) Seasonal Forecast Systems is used to predict reservoir inflows of the upper basins of the Segura and Tagus rivers. The system is evaluated for 4 seasons with a forecasting lead time of 3 months. First results show that only for certain initialization months and lead times, the developed system outperforms the reference forecast. This research is carried out within the European research project IMPREX (www.imprex.eu) that aims at investigating the value of improving predictions of hydro-meteorological extremes in a number of water sectors, including agriculture . The next step is to integrate improved seasonal forecasts into the system and evaluate these. This should finally lead to a more robust forecasting system that allows water managers and irrigators to better anticipate to drought episodes and putting into practice more effective water allocation and mitigation practices.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

Sea Ice in the NCEP Seasonal Forecast System

NASA Astrophysics Data System (ADS)

Wu, X.; Saha, S.; Grumbine, R. W.; Bailey, D. A.; Carton, J.; Penny, S. G.

2017-12-01

Sea ice is known to play a significant role in the global climate system. For a weather or climate forecast system (CFS), it is important that the realistic distribution of sea ice is represented. Sea ice prediction is challenging; sea ice can form or melt, it can move with wind and/or ocean current; sea ice interacts with both the air above and ocean underneath, it influences by, and has impact on the air and ocean conditions. NCEP has developed coupled CFS (version 2, CFSv2) and also carried out CFS reanalysis (CFSR), which includes a coupled model with the NCEP global forecast system, a land model, an ocean model (GFDL MOM4), and a sea ice model. In this work, we present the NCEP coupled model, the CFSv2 sea ice component that includes a dynamic thermodynamic sea ice model and a simple "assimilation" scheme, how sea ice has been assimilated in CFSR, the characteristics of the sea ice from CFSR and CFSv2, and the improvements of sea ice needed for future seasonal prediction system, part of the Unified Global Coupled System (UGCS), which is being developed and under testing, including sea ice data assimilation with the Local Ensemble Transform Kalman Filter (LETKF). Preliminary results from the UGCS testing will also be presented.

Dynamical downscaling inter-comparison for high resolution climate reconstruction

NASA Astrophysics Data System (ADS)

Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.

2012-04-01

In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic downscaling is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.

An Investigation of Bomb Cyclone Climatology: Reanalysis vs. NCEP's CFS Model

NASA Astrophysics Data System (ADS)

Alvarez, F. M.; Eichler, T.; Gottschalck, J.

2009-12-01

Given the concerns and potential impacts of climate change, the need for climate models to simulate weather phenomena is as important as ever. An example of such phenomena is rapidly intensifying cyclones, also known as "bombs." These intense cyclones have devastating effects on residential and marine commercial interests as well as the transportation industry. In this study, we generate a climatology of rapid cyclogenesis using the National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model. Results are compared to NCEP’s global reanalysis data to determine if the CFS model is capable of producing a realistic extreme storm climatology. This represents the first step in quantifying rapidly intensifying cyclones in the CFS model, which will be useful in contributing towards future model improvements, as well as gauging its ability in determining the role of synoptic-scale storms in climate change.

Assessment of marine weather forecasts over the Indian sector of Southern Ocean

NASA Astrophysics Data System (ADS)

Gera, Anitha; Mahapatra, D. K.; Sharma, Kuldeep; Prakash, Satya; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.; Anilkumar, N.

2017-09-01

The Southern Ocean (SO) is one of the important regions where significant processes and feedbacks of the Earth's climate take place. Expeditions to the SO provide useful data for improving global weather/climate simulations and understanding many processes. Some of the uncertainties in these weather/climate models arise during the first few days of simulation/forecast and do not grow much further. NCMRWF issued real-time five day weather forecasts of mean sea level pressure, surface winds, winds at 500 hPa & 850 hPa and rainfall, daily to NCAOR to provide guidance for their expedition to Indian sector of SO during the austral summer of 2014-2015. Evaluation of the skill of these forecasts indicates possible error growth in the atmospheric model at shorter time scales. The error growth is assessed using the model analysis/reanalysis, satellite data and observations made during the expedition. The observed variability of sub-seasonal rainfall associated with mid-latitude systems is seen to exhibit eastward propagations and are well reproduced in the model forecasts. All cyclonic disturbances including the sub-polar lows and tropical cyclones that occurred during this period were well captured in the model forecasts. Overall, this model performs reasonably well over the Indian sector of the SO in medium range time scale.

Prediction of sea ice thickness cluster in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Fuckar, Neven-Stjepan; Guemas, Virginie; Johnson, Nathaniel; Doblas-Reyes, Francisco

2016-04-01

Sea ice thickness (SIT) has a potential to contain substantial climate memory and predictability in the northern hemisphere (NH) sea ice system. We use 5-member NH SIT, reconstructed with an ocean-sea-ice general circulation model (NEMOv3.3 with LIM2) with a simple data assimilation routine, to determine NH SIT modes of variability disentangled from the long-term climate change. Specifically, we apply the K-means cluster analysis - one of nonhierarchical clustering methods that partition data into modes or clusters based on their distances in the physical - to determine optimal number of NH SIT clusters (K=3) and their historical variability. To examine prediction skill of NH SIT clusters in EC-Earth2.3, a state-of-the-art coupled climate forecast system, we use 5-member ocean and sea ice initial conditions (IC) from the same ocean-sea-ice historical reconstruction and atmospheric IC from ERA-Interim reanalysis. We focus on May 1st and Nov 1st start dates from 1979 to 2010. Common skill metrics of probability forecast, such as rank probability skill core and ROC (relative operating characteristics - hit rate versus false alarm rate) and reliability diagrams show that our dynamical model predominately perform better than the 1st order Marko chain forecast (that beats climatological forecast) over the first forecast year. On average May 1st start dates initially have lower skill than Nov 1st start dates, but their skill is degraded at slower rate than skill of forecast started on Nov 1st.

The lunar semidiurnal air pressure tide in in-situ data and ECMWF reanalyses

NASA Astrophysics Data System (ADS)

Schindelegger, Michael; Dobslaw, Henryk

2016-04-01

A gridded empirical model of the lunar semidiurnal air pressure tide L2 is deduced through multiquadric interpolation of more than 2000 globally distributed tidal estimates from land barometers and moored buoys. The resulting climatology serves as an independent standard to validate the barometric L2 oscillations that are present in ECMWF's (European Centre for Medium-Range Weather Forecasts) global atmospheric reanalyses despite the omission of gravitational forcing mechanisms in the involved forecast routines. Inconsistencies between numerical and empirical L2 solutions are found to be small even though the reanalysis models typically underestimate equatorial peak pressures by 10-20% and produce slightly deficient tidal phases in latitudes south of 30°N. Through using a time-invariant reference surface over both land and water and assimilating marine pressure data without accounting for vertical sensor movements due to the M2 ocean tide, ECMWF-based tidal solutions are also prone to strong local artifacts. Additionally, the dependency of the lunar tidal oscillation in atmospheric analysis systems on the meteorological input data is demonstrated based on a recent ECMWF twentieth-century reanalysis (ERA-20C) which draws its all of its observational constraints from in-situ registrations of pressure and surface winds. The L2 signature prior to 1950 is particularly indicative of distinct observing system changes, such as the paucity of marine data during both World Wars or the opening of the Panama Canal in 1914 and the associated adjustment of commercial shipping routes.

Extra-tropical Cyclones and Windstorms in Seasonal Forecasts

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Weisheimer, Antje; Knight, Jeff; Thornton, Hazel; Roberts, Julia; Hermanson, Leon

2015-04-01

Severe damages and large insured losses over Europe related to natural phenomena are mostly caused by extra-tropical cyclones and their related windstorm fields. Thus, an adequate representation of these events in seasonal prediction systems and reliable forecasts up to a season in advance would be of high value for society and economy. In this study, state-of-the-art seasonal forecast prediction systems are analysed (ECMWF, UK Met Office) regarding the general climatological representation and the seasonal prediction of extra-tropical cyclones and windstorms during the core winter season (DJF) with a lead time of up to four months. Two different algorithms are used to identify cyclones and windstorm events in these datasets. Firstly, we apply a cyclone identification and tracking algorithm based on the Laplacian of MSLP and secondly, we use an objective wind field tracking algorithm to identify and track continuous areas of extreme high wind speeds (cf. Leckebusch et al., 2008), which can be related to extra-tropical winter cyclones. Thus, for the first time, we can analyse the forecast of severe wind events near to the surface caused by extra-tropical cyclones. First results suggest a successful validation of the spatial climatological distributions of wind storm and cyclone occurrence in the seasonal forecast systems in comparison with reanalysis data (ECMWF-ERA40 & ERAInterim) in general. However, large biases are found for some areas. The skill of the seasonal forecast systems in simulating the year-to-year variability of the frequency of severe windstorm events and cyclones is investigated using the ranked probability skill score. Positive skill is found over large parts of the Northern Hemisphere as well as for the most intense extra-tropical cyclones and its related wind fields.

Enhancement of Local Climate Analysis Tool

NASA Astrophysics Data System (ADS)

Horsfall, F. M.; Timofeyeva, M. M.; Dutton, J.

2012-12-01

The National Oceanographic and Atmospheric Administration (NOAA) National Weather Service (NWS) will enhance its Local Climate Analysis Tool (LCAT) to incorporate specific capabilities to meet the needs of various users including energy, health, and other communities. LCAT is an online interactive tool that provides quick and easy access to climate data and allows users to conduct analyses at the local level such as time series analysis, trend analysis, compositing, correlation and regression techniques, with others to be incorporated as needed. LCAT uses principles of Artificial Intelligence in connecting human and computer perceptions on application of data and scientific techniques in multiprocessing simultaneous users' tasks. Future development includes expanding the type of data currently imported by LCAT (historical data at stations and climate divisions) to gridded reanalysis and General Circulation Model (GCM) data, which are available on global grids and thus will allow for climate studies to be conducted at international locations. We will describe ongoing activities to incorporate NOAA Climate Forecast System (CFS) reanalysis data (CFSR), NOAA model output data, including output from the National Multi Model Ensemble Prediction System (NMME) and longer term projection models, and plans to integrate LCAT into the Earth System Grid Federation (ESGF) and its protocols for accessing model output and observational data to ensure there is no redundancy in development of tools that facilitate scientific advancements and use of climate model information in applications. Validation and inter-comparison of forecast models will be included as part of the enhancement to LCAT. To ensure sustained development, we will investigate options for open sourcing LCAT development, in particular, through the University Corporation for Atmospheric Research (UCAR).

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.

Mediterranean monitoring and forecasting operational system for Copernicus Marine Service

NASA Astrophysics Data System (ADS)

Coppini, Giovanni; Drudi, Massimiliano; Korres, Gerasimos; Fratianni, Claudia; Salon, Stefano; Cossarini, Gianpiero; Clementi, Emanuela; Zacharioudaki, Anna; Grandi, Alessandro; Delrosso, Damiano; Pistoia, Jenny; Solidoro, Cosimo; Pinardi, Nadia; Lecci, Rita; Agostini, Paola; Cretì, Sergio; Turrisi, Giuseppe; Palermo, Francesco; Konstantinidou, Anna; Storto, Andrea; Simoncelli, Simona; Di Pietro, Pier Luigi; Masina, Simona; Ciliberti, Stefania Angela; Ravdas, Michalis; Mancini, Marco; Aloisio, Giovanni; Fiore, Sandro; Buonocore, Mauro

2016-04-01

The MEDiterranean Monitoring and Forecasting Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC system provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational system developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days forecasts and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and wave components of the system. Regarding the currents, new grid-point EOFs have been implemented in the Med-MFC assimilation system; the climatological CMAP precipitation was replaced by the ECMWF daily precipitation; reanalysis time-series have been increased by one year. Regarding the biogeochemistry, the main scientific achievement is related to the implementation of the carbon system in the Med-MFC biogeochemistry model system already available. The new model is able to reproduce the principal spatial patterns of the carbonate system variables in the Mediterranean Sea. Further, a key result consists of the calibration of the new variables (DIC and alkalinity), which serves to the estimation of the accuracy of the new products to be released in the next version of the system (i.e. pH and pCO2 at surface). Regarding the waves, the system has been validated against in-situ and satellite observations. For example, a very good agreement between model output and in-situ observations has been obtained at offshore and/or well-exposed wave buoys in the Mediterranean Sea.

Northern Hemisphere winter storm track trends since 1959 derived from multiple reanalysis datasets

NASA Astrophysics Data System (ADS)

Chang, Edmund K. M.; Yau, Albert M. W.

2016-09-01

In this study, a comprehensive comparison of Northern Hemisphere winter storm track trend since 1959 derived from multiple reanalysis datasets and rawinsonde observations has been conducted. In addition, trends in terms of variance and cyclone track statistics have been compared. Previous studies, based largely on the National Center for Environmental Prediction-National Center for Atmospheric Research Reanalysis (NNR), have suggested that both the Pacific and Atlantic storm tracks have significantly intensified between the 1950s and 1990s. Comparison with trends derived from rawinsonde observations suggest that the trends derived from NNR are significantly biased high, while those from the European Center for Medium Range Weather Forecasts 40-year Reanalysis and the Japanese 55-year Reanalysis are much less biased but still too high. Those from the two twentieth century reanalysis datasets are most consistent with observations but may exhibit slight biases of opposite signs. Between 1959 and 2010, Pacific storm track activity has likely increased by 10 % or more, while Atlantic storm track activity has likely increased by <10 %. Our analysis suggests that trends in Pacific and Atlantic basin wide storm track activity prior to the 1950s derived from the two twentieth century reanalysis datasets are unlikely to be reliable due to changes in density of surface observations. Nevertheless, these datasets may provide useful information on interannual variability, especially over the Atlantic.

Evaluation of atmospheric precipitable water from reanalysis products using homogenized radiosonde observations over China

NASA Astrophysics Data System (ADS)

Zhao, Tianbao; Wang, Juanhuai; Dai, Aiguo

2015-10-01

Many multidecadal atmospheric reanalysis products are available now, but their consistencies and reliability are far from perfect. In this study, atmospheric precipitable water (PW) from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), NCEP/Department of Energy (DOE), Modern Era Retrospective-Analysis for Research and Applications (MERRA), Japanese 55 year Reanalysis (JRA-55), JRA-25, ERA-Interim, ERA-40, Climate Forecast System Reanalysis (CFSR), and 20th Century Reanalysis version 2 is evaluated against homogenized radiosonde observations over China during 1979-2012 (1979-2001 for ERA-40). Results suggest that the PW biases in the reanalyses are within ˜20% for most of northern and eastern China, but the reanalyses underestimate the observed PW by 20%-40% over western China and by ˜60% over the southwestern Tibetan Plateau. The newer-generation reanalyses (e.g., JRA25, JRA55, CFSR, and ERA-Interim) have smaller root-mean-square error than the older-generation ones (NCEP/NCAR, NCEP/DOE, and ERA-40). Most of the reanalyses reproduce well the observed PW climatology and interannual variations over China. However, few reanalyses capture the observed long-term PW changes, primarily because they show spurious wet biases before about 2002. This deficiency results mainly from the discontinuities contained in reanalysis relative humidity fields in the middle-lower troposphere due to the wet bias in older radiosonde records that are assimilated into the reanalyses. An empirical orthogonal function (EOF) analysis revealed two leading modes that represent the long-term PW changes and El Niño-Southern Oscillation-related interannual variations with robust spatial patterns. The reanalysis products, especially the MERRA and JRA-25, roughly capture these EOF modes, which account for over 50% of the total variance. The results show that even during the post-1979 satellite era, discontinuities in radiosonde data can still induce large spurious long-term changes in reanalysis PW and other related fields. Thus, more efforts are needed to remove spurious changes in input data for future long-term reanalyses.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi

2011-01-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Software Framework for Development of Web-GIS Systems for Analysis of Georeferenced Geophysical Data

NASA Astrophysics Data System (ADS)

Okladnikov, I.; Gordov, E. P.; Titov, A. G.

2011-12-01

Georeferenced datasets (meteorological databases, modeling and reanalysis results, remote sensing products, etc.) are currently actively used in numerous applications including modeling, interpretation and forecast of climatic and ecosystem changes for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their size which might constitute up to tens terabytes for a single dataset at present studies in the area of climate and environmental change require a special software support. A dedicated software framework for rapid development of providing such support information-computational systems based on Web-GIS technologies has been created. The software framework consists of 3 basic parts: computational kernel developed using ITTVIS Interactive Data Language (IDL), a set of PHP-controllers run within specialized web portal, and JavaScript class library for development of typical components of web mapping application graphical user interface (GUI) based on AJAX technology. Computational kernel comprise of number of modules for datasets access, mathematical and statistical data analysis and visualization of results. Specialized web-portal consists of web-server Apache, complying OGC standards Geoserver software which is used as a base for presenting cartographical information over the Web, and a set of PHP-controllers implementing web-mapping application logic and governing computational kernel. JavaScript library aiming at graphical user interface development is based on GeoExt library combining ExtJS Framework and OpenLayers software. Based on the software framework an information-computational system for complex analysis of large georeferenced data archives was developed. Structured environmental datasets available for processing now include two editions of NCEP/NCAR Reanalysis, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis, ECMWF ERA Interim Reanalysis, MRI/JMA APHRODITE's Water Resources Project Reanalysis, meteorological observational data for the territory of the former USSR for the 20th century, and others. Current version of the system is already involved into a scientific research process. Particularly, recently the system was successfully used for analysis of Siberia climate changes and its impact in the region. The software framework presented allows rapid development of Web-GIS systems for geophysical data analysis thus providing specialists involved into multidisciplinary research projects with reliable and practical instruments for complex analysis of climate and ecosystems changes on global and regional scales. This work is partially supported by RFBR grants #10-07-00547, #11-05-01190, and SB RAS projects 4.31.1.5, 4.31.2.7, 4, 8, 9, 50 and 66.

Evaluation and Improvement of Polar WRF simulations using the observed atmospheric profiles in the Arctic seasonal ice zone

NASA Astrophysics Data System (ADS)

Liu, Z.; Schweiger, A. J. B.

2016-12-01

We use the Polar Weather Research and Forecasting (WRF) model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) over the Beaufort Sea in the summer since 2013. With the 119 SIZRS dropsondes in the18 cross sections along the 150W and 140W longitude lines, we evaluate the performance of WRF simulations and two forcing data sets, the ERA-Interim reanalysis and the Global Forecast System (GFS) analysis, and explore the improvement of the Polar WRF performance when the dropsonde data are assimilated using observation nudging. Polar WRF, ERA-Interim, and GFS can reproduce the general features of the observed mean atmospheric profiles, such as low-level temperature inversion, low-level jet (LLJ) and specific humidity inversion. The Polar WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing, which is likely related to the lower values of the boundary layer diffusion in WRF than in the global models such as ECMWF and GFS. The Polar WRF simulated relative humidity closely resembles the forcing datasets while having large biases compared to observations. This suggests that the performance of Polar WRF and its forecasts in this region are limited by the quality of the forcing dataset and that the assimilation of more and better-calibrated observations, such as humidity data, is critical for their improvement. We investigate the potential of assimilating the SIZRS dropsonde dataset in improving the weather forecast over the Beaufort Sea. A simple local nudging approach is adopted. Along SIZRS flight cross sections, a set of Polar WRF simulations are performed with varying number of variables and dropsonde profiles assimilated. Different model physics are tested to examine the sensitivity of different aspects of model physics, such as boundary layer schemes, cloud microphysics, and radiation parameterization, to data assimilation. The comparison of the Polar WRF runs with assimilation and the runs without assimilation demonstrates the importance of SIZRS dropsonde data to the improvement of atmospheric analysis and reanalysis such as GFS and ERA-Interim, and consequently to the improvement of weather forecast in this region.

C-GLORSv5: an improved multipurpose global ocean eddy-permitting physical reanalysis

NASA Astrophysics Data System (ADS)

Storto, Andrea; Masina, Simona

2016-11-01

Global ocean reanalyses combine in situ and satellite ocean observations with a general circulation ocean model to estimate the time-evolving state of the ocean, and they represent a valuable tool for a variety of applications, ranging from climate monitoring and process studies to downstream applications, initialization of long-range forecasts and regional studies. The purpose of this paper is to document the recent upgrade of C-GLORS (version 5), the latest ocean reanalysis produced at the Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) that covers the meteorological satellite era (1980-present) and it is being updated in delayed time mode. The reanalysis is run at eddy-permitting resolution (1/4° horizontal resolution and 50 vertical levels) and consists of a three-dimensional variational data assimilation system, a surface nudging and a bias correction scheme. With respect to the previous version (v4), C-GLORSv5 contains a number of improvements. In particular, background- and observation-error covariances have been retuned, allowing a flow-dependent inflation in the globally averaged background-error variance. An additional constraint on the Arctic sea-ice thickness was introduced, leading to a realistic ice volume evolution. Finally, the bias correction scheme and the initialization strategy were retuned. Results document that the new reanalysis outperforms the previous version in many aspects, especially in representing the variability of global heat content and associated steric sea level in the last decade, the top 80 m ocean temperature biases and root mean square errors, and the Atlantic Ocean meridional overturning circulation; slight worsening in the high-latitude salinity and deep ocean temperature emerge though, providing the motivation for further tuning of the reanalysis system. The dataset is available in NetCDF format at doi:10.1594/PANGAEA.857995.

CCPP-ARM Parameterization Testbed Model Forecast Data

DOE Data Explorer

Klein, Stephen

2008-01-15

Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

A Comparison of Five Numerical Weather Prediction Analysis Climatologies in Southern High Latitudes.

NASA Astrophysics Data System (ADS)

Connolley, William M.; Harangozo, Stephen A.

2001-01-01

In this paper, numerical weather prediction analyses from four major centers are compared-the Australian Bureau of Meteorology (ABM), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), and The Met. Office (UKMO). Two of the series-ECMWF reanalysis (ERA) and NCEP-NCAR reanalysis (NNR)-are `reanalyses'; that is, the data have recently been processed through a consistent, modern analysis system. The other three-ABM, ECMWF operational (EOP), and UKMO-are archived from operational analyses.The primary focus in this paper is on the period of 1979-93, the period used for the reanalyses, and on climatology. However, ABM and NNR are also compared for the period before 1979, for which the evidence tends to favor NNR. The authors are concerned with basic variables-mean sea level pressure, height of the 500-hPa surface, and near-surface temperature-that are available from the basic analysis step, rather than more derived quantities (such as precipitation), which are available only from the forecast step.Direct comparisons against station observations, intercomparisons of the spatial pattern of the analyses, and intercomparisons of the temporal variation indicate that ERA, EOP, and UKMO are best for sea level pressure;that UKMO and EOP are best for 500-hPa height; and that none of the analyses perform well for near-surface temperature.

Evaluation of the Analysis Influence on Transport in Reanalysis Regional Water Cycles

NASA Technical Reports Server (NTRS)

Bosilovich, M. G.; Chen, J.; Robertson, F. R.

2011-01-01

Regional water cycles of reanalyses do not follow theoretical assumptions applicable to pure simulated budgets. The data analysis changes the wind, temperature and moisture, perturbing the theoretical balance. Of course, the analysis is correcting the model forecast error, so that the state fields should be more aligned with observations. Recently, it has been reported that the moisture convergence over continental regions, even those with significant quantities of radiosonde profiles present, can produce long term values not consistent with theoretical bounds. Specifically, long averages over continents produce some regions of moisture divergence. This implies that the observational analysis leads to a source of water in the region. One such region is the Unite States Great Plains, which many radiosonde and lidar wind observations are assimilated. We will utilize a new ancillary data set from the MERRA reanalysis called the Gridded Innovations and Observations (GIO) which provides the assimilated observations on MERRA's native grid allowing more thorough consideration of their impact on regional and global climatology. Included with the GIO data are the observation minus forecast (OmF) and observation minus analysis (OmA). Using OmF and OmA, we can identify the bias of the analysis against each observing system and gain a better understanding of the observations that are controlling the regional analysis. In this study we will focus on the wind and moisture assimilation.

Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2017-03-01

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature

NASA Astrophysics Data System (ADS)

Materia, Stefano; Borrelli, Andrea; Bellucci, Alessio; Alessandri, Andrea; Di Pietro, Pierluigi; Athanasiadis, Panagiotis; Navarra, Antonio; Gualdi, Silvio

2014-05-01

The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through land surface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of land surface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for land surface initialization are required for more accurate seasonal forecasts.

Metric optimisation for analogue forecasting by simulated annealing

NASA Astrophysics Data System (ADS)

Bliefernicht, J.; Bárdossy, A.

2009-04-01

It is well known that weather patterns tend to recur from time to time. This property of the atmosphere is used by analogue forecasting techniques. They have a long history in weather forecasting and there are many applications predicting hydrological variables at the local scale for different lead times. The basic idea of the technique is to identify past weather situations which are similar (analogue) to the predicted one and to take the local conditions of the analogues as forecast. But the forecast performance of the analogue method depends on user-defined criteria like the choice of the distance function and the size of the predictor domain. In this study we propose a new methodology of optimising both criteria by minimising the forecast error with simulated annealing. The performance of the methodology is demonstrated for the probability forecast of daily areal precipitation. It is compared with a traditional analogue forecasting algorithm, which is used operational as an element of a hydrological forecasting system. The study is performed for several meso-scale catchments located in the Rhine basin in Germany. The methodology is validated by a jack-knife method in a perfect prognosis framework for a period of 48 years (1958-2005). The predictor variables are derived from the NCEP/NCAR reanalysis data set. The Brier skill score and the economic value are determined to evaluate the forecast skill and value of the technique. In this presentation we will present the concept of the optimisation algorithm and the outcome of the comparison. It will be also demonstrated how a decision maker should apply a probability forecast to maximise the economic benefit from it.

Downscaled ice-ocean simulations for the Chukchi and Eastern Siberian Seas from an oceanic re-analysis product

NASA Astrophysics Data System (ADS)

Fujisaki-Manome, A.; Wang, J.

2016-12-01

Arctic summer sea ice has been declining at the rate that is much faster than any climate models predict. While the accelerated sea ice melting in the recent few decades could be attributed to several mechanisms such as the Arctic temperature amplification and the ice-albedo feedback, this does not necessarily explain why climate models underestimate the observed rate of summer sea ice loss. Clearly, an improved understanding is needed in what processes could be missed in climate models and could play roles in unprecedented loss of sea ice. This study evaluates contributions of sub-mesoscale processes in the ice edge (i.e. the boundary region between open water and ice covered area), which include eddies, ice bands, and the vertical mixing associated with ice bands, to the melting of sea ice and how they explain the underestimation of sea ice loss in the current state-of-art climate models. The focus area is in the pacific side of the Arctic Ocean. First, several oceanic re-analysis products including NCEP-Climate Forecast System Reanalysis (CFSR) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) are evaluated in comparison with the in-situ observations from the Russian-American Long-term Census of the Arctic (RUSALCA) project. Second, the downscaled ice-ocean simulations are conducted for the Chukchi and East Siberian Seas with initial and open boundary conditions provided from a selected oceanic re-analysis product.

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

NASA Astrophysics Data System (ADS)

Akperov, Mirseid; Rinke, Annette; Mokhov, Igor I.; Matthes, Heidrun; Semenov, Vladimir A.; Adakudlu, Muralidhar; Cassano, John; Christensen, Jens H.; Dembitskaya, Mariya A.; Dethloff, Klaus; Fettweis, Xavier; Glisan, Justin; Gutjahr, Oliver; Heinemann, Günther; Koenigk, Torben; Koldunov, Nikolay V.; Laprise, René; Mottram, Ruth; Nikiéma, Oumarou; Scinocca, John F.; Sein, Dmitry; Sobolowski, Stefan; Winger, Katja; Zhang, Wenxin

2018-03-01

The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.

Gigantic Jet Environments: A Meteorological Evaluation Using Reanalysis Data Sets

NASA Astrophysics Data System (ADS)

Splitt, M. E.; Lazarus, S. M.

2017-12-01

The meteorological conditions of gigantic jet (GJ) producing thunderstorms tend to be connected to maritime tropical environments. In particular, they have an affinity toward tropical disturbances including those with moderate values of upper tropospheric environmental wind shear. Wind shear related effects (including turbulence) in association with deep convection in these environments have been proposed as mechanisms for the arrangement of GJ favorable charge structures. This study focuses on a climatological evaluation in an effort to assess whether the proposed ingredients are consistent with observed GJ event regions. The Climate System Forecast System - Version 2 (CFSR V2) is used here to test for the proposed GJ conditions.

Reconstructing paleoclimate fields using online data assimilation with a linear inverse model

NASA Astrophysics Data System (ADS)

Perkins, Walter A.; Hakim, Gregory J.

2017-05-01

We examine the skill of a new approach to climate field reconstructions (CFRs) using an online paleoclimate data assimilation (PDA) method. Several recent studies have foregone climate model forecasts during assimilation due to the computational expense of running coupled global climate models (CGCMs) and the relatively low skill of these forecasts on longer timescales. Here we greatly diminish the computational cost by employing an empirical forecast model (linear inverse model, LIM), which has been shown to have skill comparable to CGCMs for forecasting annual-to-decadal surface temperature anomalies. We reconstruct annual-average 2 m air temperature over the instrumental period (1850-2000) using proxy records from the PAGES 2k Consortium Phase 1 database; proxy models for estimating proxy observations are calibrated on GISTEMP surface temperature analyses. We compare results for LIMs calibrated using observational (Berkeley Earth), reanalysis (20th Century Reanalysis), and CMIP5 climate model (CCSM4 and MPI) data relative to a control offline reconstruction method. Generally, we find that the usage of LIM forecasts for online PDA increases reconstruction agreement with the instrumental record for both spatial fields and global mean temperature (GMT). Specifically, the coefficient of efficiency (CE) skill metric for detrended GMT increases by an average of 57 % over the offline benchmark. LIM experiments display a common pattern of skill improvement in the spatial fields over Northern Hemisphere land areas and in the high-latitude North Atlantic-Barents Sea corridor. Experiments for non-CGCM-calibrated LIMs reveal region-specific reductions in spatial skill compared to the offline control, likely due to aspects of the LIM calibration process. Overall, the CGCM-calibrated LIMs have the best performance when considering both spatial fields and GMT. A comparison with the persistence forecast experiment suggests that improvements are associated with the linear dynamical constraints of the forecast and not simply persistence of temperature anomalies.

Application of Humidity Data for Predictions of Influenza Outbreaks.

NASA Astrophysics Data System (ADS)

Teixeira, J.; Thrastarson, H. T.; Yeo, E.

2016-12-01

Seasonal influenza outbreaks infect millions of people, cause hundreds of thousands of deaths worldwide, and leave an immense economic footprint. Potential forecasting of the timing and intensity of these outbreaks can help mitigation and response efforts (e.g., the management and organization of vaccines, drugs and other resources). Absolute (or specific) humidity has been identified as an important driver of the seasonal behavior of influenza outbreaks in temperate regions. Building upon this result, we incorporate humidity data from both NASA's AIRS (Atmospheric Infra-Red Sounder) instrument and ERA-Interim re-analysis into a SIRS (Susceptible-Infectious-Recovered-Susceptible) type numerical epidemiological model, comprising a prediction system for influenza outbreaks. Data for influenza activity is obtained from sources such as Google Flu Trends and the CDC (Center for Disease Control) and used for comparison and assimilation. The accuracy and limitations of the prediction system are tested with hindcasts of outbreaks in the United States for the years 2005-2015. Our results give support to the hypothesis that local weather conditions drive the seasonality of influenza in temperate regions. The implementation of influenza forecasts that make use of NCEP humidity forecasts is also discussed.

Error discrimination of an operational hydrological forecasting system at a national scale

NASA Astrophysics Data System (ADS)

Jordan, F.; Brauchli, T.

2010-09-01

The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System

The skill of ECMWF long range Forecasting System to drive impact models for health and hydrology in Africa

NASA Astrophysics Data System (ADS)

Di Giuseppe, F.; Tompkins, A. M.; Lowe, R.; Dutra, E.; Wetterhall, F.

2012-04-01

As the quality of numerical weather prediction over the monthly to seasonal leadtimes steadily improves there is an increasing motivation to apply these fruitfully to the impacts sectors of health, water, energy and agriculture. Despite these improvements, the accuracy of fields such as temperature and precipitation that are required to drive sectoral models can still be poor. This is true globally, but particularly so in Africa, the region of focus in the present study. In the last year ECMWF has been particularly active through EU research founded projects in demonstrating the capability of its longer range forecasting system to drive impact modeling systems in this region. A first assessment on the consequences of the documented errors in ECMWF forecasting system is therefore presented here looking at two different application fields which we found particularly critical for Africa - vector-born diseases prevention and hydrological monitoring. A new malaria community model (VECTRI) has been developed at ICTP and tested for the 3 target regions participating in the QWECI project. The impacts on the mean malaria climate is assessed using the newly realized seasonal forecasting system (Sys4) with the dismissed system 3 (Sys3) which had the same model cycle of the up-to-date ECMWF re-analysis product (ERA-Interim). The predictive skill of Sys4 to be employed for malaria monitoring and forecast are also evaluated by aggregating the fields to country level. As a part of the DEWFORA projects, ECMWF is also developing a system for drought monitoring and forecasting over Africa whose main meteorological input is precipitation. Similarly to what is done for the VECTRI model, the skill of seasonal forecasts of precipitation is, in this application, translated into the capability of predicting drought while ERA-Interim is used in monitoring. On a monitoring level, the near real-time update of ERA-Interim could compensate the lack of observations in the regions. However, ERA-Interim suffers from biases and drifts that limit its application for drought monitoring purposes in some regions.

Data Integration Plans for the NOAA National Climate Model Portal (NCMP) (Invited)

NASA Astrophysics Data System (ADS)

Rutledge, G. K.; Williams, D. N.; Deluca, C.; Hankin, S. C.; Compo, G. P.

2010-12-01

NOAA’s National Climatic Data Center (NCDC) and its collaborators have initiated a five-year development and implementation of an operational access capability for the next generation weather and climate model datasets. The NOAA National Climate Model Portal (NCMP) is being designed using format neutral open web based standards and tools where users at all levels of expertise can gain access and understanding to many of NOAA’s climate and weather model products. NCMP will closely coordinate with and reside under the emerging NOAA Climate Services Portal (NCSP). To carry out its mission, NOAA must be able to successfully integrate model output and other data and information from all of its discipline specific areas to understand and address the complexity of many environmental problems. The NCMP will be an initial access point for the emerging NOAA Climate Services Portal (NCSP), which is the basis for unified access to NOAA climate products and services. NCMP is currently collaborating with the emerging Environmental Projection Center (EPC) expected to be developed at the Earth System Research Laboratory in Boulder CO. Specifically, NCMP is being designed to: - Enable policy makers and resource managers to make informed national and global policy decisions using integrated climate and weather model outputs, observations, information, products, and other services for the scientist and the non-scientist; - Identify model to observational interoperability requirements for climate and weather system analysis and diagnostics; - Promote the coordination of an international reanalysis observational clearinghouse (i.e.., Reanalysis.org) spanning the worlds numerical processing Center’s for an “Ongoing Analysis of the Climate System”. NCMP will initially provide access capabilities to 3 of NOAA’s high volume Reanalysis data sets of the weather and climate systems: 1) NCEP’s Climate Forecast System Reanalysis (CFS-R); 2) NOAA’s Climate Diagnostics Center/ Earth System Research Laboratory (ESRL) Twentieth Century Reanalysis Project data set (20CR, G. Compo, et al.), a historical reanalysis that will provide climate information dating back to 1850 to the present; and 3) the CPC’s Upper Air Reanlaysis. NCMP will advance the highly successful NOAA National Operational Model Archive and Distribution System (NOMADS, Rutledge, BAMS 2006), and standards already in use including Unidata’s THREDDS (TDS), PMEL’s Live Access Server (LAS) and the GrADS Data Server (GDS) from COLA; the Department of Energy (DOE) Earth System Grid (ESG) and the associated IPCC Climate model archive located at the Program for Climate Model Diagnostics and Inter-comparison (PCMDI) through the ESG; and NOAA’s Unified Access Framework (UAF) effort; and core standards developed by Open Geospatial Consortium (OGC). The format neutral OPeNDAP protocol as used in the NOMADS system will also be a key aspect of the design of NCMP.

Climatology and Structures of Southwest Vortices in NCEP Climate Forecast System Reanalysis

NASA Astrophysics Data System (ADS)

Feng, Xinyuan; Liu, Changhai; Fan, Guangzhou; Liu, Xiaodong; Feng, Caiyun

2017-04-01

A southwest vortex (SWV) refers to the meso-α-scale cyclonic low-pressure system originating in southwest China, as a result of interactions of large-scale circulations and the specific multi-scale topography, such as the Tibetan Plateau, Hengduan Mountain and Sichuan Basin. It is a high-impact precipitation-generating weather system in southwestern China, in the Yangtze River valley and even in north China. This paper reports on a systematic investigation of its climatological and structural characteristics over the 32-yr period of 1979-2010 using the high-resolution NCEP Climate Forecast System Reanalysis data. The present study has the several unique features. First, the new generation reanalysis product possesses high spatial and temporal resolution, arguably being more suitable for mesoscale vortex studies as compared to the preceding reanalysis datasets and moreover enabling an examination of the diurnal behavior. Second, our 32-yr statistics are capable of producing a robust representation of the SWV climatology. Third, the application of an objective identification methodology avoids some subjective ambiguities in the manual approach that has exclusively been adopted before. Lastly, a systematic exploration of thermodynamic and kinematic structures is conducted, unlike the previous exclusive heavy-rain-generating case studies. Our major findings are summarized as follows. The SWV is a common regional weather system with an annual count of 73. Two primary source regions are identified, located in the Sichuan Basin and southeast flank of the Tibetan Plateau, respectively. The genesis displays striking seasonality, characteristic of a spring-summer (March-August) preference with a peak in May. Remarkable diurnal variations are present, with two active periods around 07 and 19 Local Time. There exist prominent regional disparities in both the seasonal and diurnal variability though. A large portion of the vortices travel a rather limited distance due partially to their short persistence. The average duration time, horizontal dimension (effective diameter), and translation speed are 15.1 h, 435 km, and 8.6 m s-1, respectively. The SWV structures show regional and seasonal contrasts. The winter-spring elevated dry vortex in the basin is vertically confined to a shallow layer between 850-600 hPa and tilts northeastward. The low level has a cold center, and the mid-upper levels feature apparent baroclinicity. The nighttime warm-season precipitating vortex system in the basin has a deep structure with the cyclonic vorticity extending from the surface into the upper-troposphere. The non-severe precipitating vortex is weakly baroclinic and tilts northward with height, whereas the severe precipitating vortex is vertically aligned. In the southern mountainous region, the shallow surface-based vortex develops in a well-mixed planetary boundary layer during the evening-early-night time and exhibits vertical tilting toward the elevated upslope and a warm and low-humidity core. When attendant with precipitation, the mountainous system is large, deep and nearly upright at most levels with a fairly barotropic environment.

The Mercator Océan operational and reanalysis systems: overview of recent improvements and scientific key issues

NASA Astrophysics Data System (ADS)

Hernandez, F.; Benkiran, M.; Bourdalle-Badie, R.; Bricaud, C.; Cailleau, S.; Chanut, J.; Desportes, C.; Dombrowsky, E.; drevillon, M.; Drillet, Y.; Elmoussaoui, A.; Ferry, N.; Garric, G.; Greiner, E.; Le Galloudec, O.; Lellouche, J.; Levier, B.; Parent, L.; Perruche, C.; Reffray, G.; Regnier, C.; Rémy, E.; Testut, C.; Tranchant, B.

2011-12-01

In the framework of the European project GMES/MyOcean, Mercator Océan has designed a hierarchy of ocean analysis, forecasting and reanalysis systems based on numerical models of the ocean/sea-ice, data assimilation methods and biogeochemistry model. Operational weekly analysis provide initial conditions for daily predictions. All ocean model configurations are based on NEMO. The 1/4° global and 1/12° Atlantic and Mediterranean configurations are improved with (i) the use of high frequency (3h) atmospheric forcings including the diurnal cycle, (ii) the use of the CORE bulk formulation, (iii) the use of a new TKE vertical mixing scheme, (iv) the use of the LIM2-EVP ice model. Leading to a better representation of the diurnal cycle, the stratification in upper layers, the sea-ice interannual extensions, or mesoscale features and WBC in the 1/12°. The 1/36° IBI regional configuration, adds non linear free surface, atmospheric pressure and tidal forcing, barotropic/baroclinic time splitting, and specific boundary conditions for operational nesting with the global system. At regional scale, a number of improvements (bathymetry and bottom friction) make it suitable for coastal modelling, validated state-of-art coastal ocean models. Data assimilation is based on reduced order Kalman filter using 3D multivariate modal decomposition of the forecast error. It assimilates jointly satellite altimetry, SST and in situ observations (temperature and salinity profiles, including ARGO data). Among difficulties, the assimilation has to be operated in real time, with limited and less accurate set of observations. Recent improvements in the global systems include (v) the insertion of the zonal/meridional velocity components into the control vector, (vi) the use of the IAU procedure, (vii) the insertion of new observational operators, (viii) the use of a new MDT, (ix) the introduction of pseudo-observations, (x) the use of a bias correction method based on a variational approach to estimate large scale biases. These improvements limit noise introduced by sequential assimilation, like in in the vertical dynamics. Water masses, on the shelves or near major run-off, like the Amazon discharge, are preserved. A biogeochemistry prediction system has been added, based on PISCES model. it uses spatial degradation of the real time physic provides by the 1/4° global system. A reanalysis covering the "altimetric era" (1992-2009) has been carried out in collaboration with the Drakkar community. GLORYS reanalyses describe the evolution of the ocean and sea-ice states; It is based on Mercator operational 1/4° global system, but with 75 levels vertical grid, ERA-Interim atmospheric forcing fields (corrected with satellite-based fluxes) and the assimilation of delayed time reprocessed and quality controlled observations. First studies show the reanalysis usefulness for interannual assessment of the mesoscale dynamics, but also the thermohaline circulations changes like MOC.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Objective tropical cyclone extratropical transition detection in high-resolution reanalysis and climate model data

DOE PAGES

Zarzycki, Colin M.; Thatcher, Diana R.; Jablonowski, Christiane

2017-01-22

This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high-resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable-resolution Community Atmosphere Model (CAM) at two different resolutions (DX 55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, DX 38 km), and the ERA-Interim Reanalysis (ERA-I, DX 80 km).more » The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA-I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold-core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase.« less

Effects of temperature on flood forecasting: analysis of an operative case study in Alpine basins

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Salandin, A.; Rabuffetti, D.; Montani, A.; Borgonovo, E.; Mancini, M.

2013-04-01

In recent years the interest in the forecast and prevention of natural hazards related to hydro-meteorological events has increased the challenge for numerical weather modelling, in particular for limited area models, to improve the quantitative precipitation forecasts (QPF) for hydrological purposes. After the encouraging results obtained in the MAP D-PHASE Project, we decided to devote further analyses to show recent improvements in the operational use of hydro-meteorological chains, and above all to better investigate the key role played by temperature during snowy precipitation. In this study we present a reanalysis simulation of one meteorological event, which occurred in November 2008 in the Piedmont Region. The attention is focused on the key role of air temperature, which is a crucial feature in determining the partitioning of precipitation in solid and liquid phase, influencing the quantitative discharge forecast (QDF) into the Alpine region. This is linked to the basin ipsographic curve and therefore by the total contributing area related to the snow line of the event. In order to assess hydrological predictions affected by meteorological forcing, a sensitivity analysis of the model output was carried out to evaluate different simulation scenarios, considering the forecast effects which can radically modify the discharge forecast. Results show how in real-time systems hydrological forecasters have to consider also the temperature uncertainty in forecasts in order to better understand the snow dynamics and its effect on runoff during a meteorological warning with a crucial snow line over the basin. The hydrological ensemble forecasts are based on the 16 members of the meteorological ensemble system COSMO-LEPS (developed by ARPA-SIMC) based on the non-hydrostatic model COSMO, while the hydrological model used to generate the runoff simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano.

Assimilation of Ocean-Color Plankton Functional Types to Improve Marine Ecosystem Simulations

NASA Astrophysics Data System (ADS)

Ciavatta, S.; Brewin, R. J. W.; Skákala, J.; Polimene, L.; de Mora, L.; Artioli, Y.; Allen, J. I.

2018-02-01

We assimilated phytoplankton functional types (PFTs) derived from ocean color into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton, and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998-2003. The skill of the reference and reanalysis simulations in estimating ocean color and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean-color PFTs (except nanoplankton), as well as the not-assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not-assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air-sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf-sea ecosystems.

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Pfaendtner, James; Bloom, Stephen; Lamich, David; Seablom, Michael; Sienkiewicz, Meta; Stobie, James; Dasilva, Arlindo

1995-01-01

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Towards real-time flood forecasting in hydraulics: merits of in situ discharge and water level data assimilation for the modeling of the Marne catchment in France

NASA Astrophysics Data System (ADS)

Ricci, S. M.; Habert, J.; Le Pape, E.; Piacentini, A.; Jonville, G.; Thual, O.; Zaoui, F.

2011-12-01

The present study describes the assimilation of river flow and water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of the one-dimensional hydraulic model, MASCARET, [1] which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was previously applied on the Adour Maritime Catchment using water level observations [2]. On average, it was shown that the data assimilation procedure enables an improvement of 80% in the simulated water level over the reanalysis period, 60 % in the forecast water level at a one-hour lead time, and 25% at a twelve-hour lead time. The procedure was then applied on the Marne Catchment, which includes karstic tributaries, located East of the Paris basin, characterized by long flooding periods and strong sensitivity to local precipitations. The objective was to geographically extend and improve the existing model used by the flood forecasting service located in Chalons-en-Champagne. A hydrological study first enabled the specification of boundary conditions (upstream flow or lateral inflow), then the hydraulic model was calibrated using in situ discharge data (adjustment of Strickler coefficients or cross sectional geometry). The assimilation of water level data enabled the reduction of the uncertainty in the hydrological boundary conditions and led to significant improvement of the simulated water level in re-analysis and forecast modes. Still, because of errors in the Strickler coefficients or cross section geometry, the improvement of the simulated water level sometimes resulted in a degradation of discharge values. This problem was overcome by controlling the correction of the hydrological boundary conditions by directly assimilating discharge observations rather than water level observations. As this approach leads to a satisfying simulation of flood events in the Marne catchment in re-analysis and forecast mode, ongoing work aims at controlling Strickler coefficients through data assimilation procedures in order to simultaneously improve the water level and discharge state. [1] N. Goutal, F. Maurel: A finite volume solver for 1D shallow water equations applied to an actual river, Int. J. Numer. Meth. Fluids, 38(2), 1--19, 2002. [2] S. Ricci, A. Piacentini, O. Thual, E. Le Pape, G. Jonville, 2011: Correction of upstream flow and hydraulic state with data assimilation on the context of flood forecasting. Submitted to Hydrol. Earth Syst. Sci, In review.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

2017-07-01

For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Remote sensing of atmospheric water vapor from synthetic aperture radar interferometry: case studies in Shanghai, China

NASA Astrophysics Data System (ADS)

Chang, Liang; Liu, Min; Guo, Lixin; He, Xiufeng; Gao, Guoping

2016-10-01

The estimation of atmospheric water vapor with high resolution is important for operational weather forecasting, climate monitoring, atmospheric research, and numerous other applications. The 40 m×40 m and 30 m×30 m differential precipitable water vapor (ΔPWV) maps are generated with C- and L-band synthetic aperture radar interferometry (InSAR) images over Shanghai, China, respectively. The ΔPWV maps are accessed via comparisons with the spatiotemporally synchronized PWV measurements from the European Centre for Medium-Range Weather Forecasts Interim reanalysis at the finest resolution and global positioning system observations, respectively. Results reveal that the ΔPWV maps can be estimated from both C- and L-band InSAR images with an accuracy of better than 2.0 mm, which, therefore, demonstrates the ability of InSAR observations at both C- and L-band to detect the water vapor distribution with high spatial resolution.

Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Fulakeza, Matthew B.

2013-01-01

Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation ensembles is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional Downscaling Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each downscaling system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each downscaling system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the downscaling, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Shukla, Shraddhanand; Arsenault, Kristi R.; Getirana, Augusto; Kumar, Sujay V.; Roningen, Jeanne; Zaitchik, Ben; McNally, Amy; Koster, Randal D.; Peters-Lidard, Christa

2017-04-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. A seamless and effective monitoring and early warning system is needed by regional/national stakeholders. Such system should support a proactive drought management approach and mitigate the socio-economic losses up to the extent possible. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of the LIS models used for drought and water availability monitoring in the region. The second part will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the monitoring and forecasting products through NASA's web-services. The water deficit forecasting system thus far incorporates NOAA's Noah land surface model (LSM), version 3.3, the Variable Infiltration Capacity (VIC) model, version 4.12, NASA GMAO's Catchment LSM, and the Noah Multi-Physics (MP) LSM (the latter two incorporate prognostic water table schemes). In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. The LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. The LIS software framework integrates these forcing datasets and drives the four LSMs and HyMAP. The Land Verification Toolkit (LVT) is used for the evaluation of the LSMs, as it provides model ensemble metrics and the ability to compare against a variety of remotely sensed measurements, like different evapotranspiration (ET) and soil moisture products, and other reanalysis datasets that are available for this region. Comparison of the models' energy and hydrological budgets will be shown for this region (and sub-basin level, e.g., Blue Nile River) and time period (1981-2015), along with evaluating ET, streamflow, groundwater storage and soil moisture, using evaluation metrics (e.g., anomaly correlation, RMSE, etc.). The system uses seasonal climate forecasts from NASA's GMAO (the Goddard Earth Observing System Model, version 5) and NCEP's Climate Forecast System, version 2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region.

Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States

NASA Astrophysics Data System (ADS)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.; Kotamarthi, V. Rao

2018-02-01

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 × 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmental Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation's performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.

Seasonal drought predictability in Portugal using statistical-dynamical techniques

NASA Astrophysics Data System (ADS)

Ribeiro, A. F. S.; Pires, C. A. L.

2016-08-01

Atmospheric forecasting and predictability are important to promote adaption and mitigation measures in order to minimize drought impacts. This study estimates hybrid (statistical-dynamical) long-range forecasts of the regional drought index SPI (3-months) over homogeneous regions from mainland Portugal, based on forecasts from the UKMO operational forecasting system, with lead-times up to 6 months. ERA-Interim reanalysis data is used for the purpose of building a set of SPI predictors integrating recent past information prior to the forecast launching. Then, the advantage of combining predictors with both dynamical and statistical background in the prediction of drought conditions at different lags is evaluated. A two-step hybridization procedure is performed, in which both forecasted and observed 500 hPa geopotential height fields are subjected to a PCA in order to use forecasted PCs and persistent PCs as predictors. A second hybridization step consists on a statistical/hybrid downscaling to the regional SPI, based on regression techniques, after the pre-selection of the statistically significant predictors. The SPI forecasts and the added value of combining dynamical and statistical methods are evaluated in cross-validation mode, using the R2 and binary event scores. Results are obtained for the four seasons and it was found that winter is the most predictable season, and that most of the predictive power is on the large-scale fields from past observations. The hybridization improves the downscaling based on the forecasted PCs, since they provide complementary information (though modest) beyond that of persistent PCs. These findings provide clues about the predictability of the SPI, particularly in Portugal, and may contribute to the predictability of crops yields and to some guidance on users (such as farmers) decision making process.

Application of Observed Precipitation in NCEP Global and Regional Data Assimilation Systems, Including Reanalysis and Land Data Assimilation

NASA Astrophysics Data System (ADS)

Mitchell, K. E.

2006-12-01

The Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) applies several different analyses of observed precipitation in both the data assimilation and validation components of NCEP's global and regional numerical weather and climate prediction/analysis systems (including in NCEP global and regional reanalysis). This invited talk will survey these data assimilation and validation applications and methodologies, as well as the temporal frequency, spatial domains, spatial resolution, data sources, data density and data quality control in the precipitation analyses that are applied. Some of the precipitation analyses applied by EMC are produced by NCEP's Climate Prediction Center (CPC), while others are produced by the River Forecast Centers (RFCs) of the National Weather Service (NWS), or by automated algorithms of the NWS WSR-88D Radar Product Generator (RPG). Depending on the specific type of application in data assimilation or model forecast validation, the temporal resolution of the precipitation analyses may be hourly, daily, or pentad (5-day) and the domain may be global, continental U.S. (CONUS), or Mexico. The data sources for precipitation include ground-based gauge observations, radar-based estimates, and satellite-based estimates. The precipitation analyses over the CONUS are analyses of either hourly, daily or monthly totals of precipitation, and they are of two distinct types: gauge-only or primarily radar-estimated. The gauge-only CONUS analysis of daily precipitation utilizes an orographic-adjustment technique (based on the well-known PRISM precipitation climatology of Oregon State University) developed by the NWS Office of Hydrologic Development (OHD). The primary NCEP global precipitation analysis is the pentad CPC Merged Analysis of Precipitation (CMAP), which blends both gauge observations and satellite estimates. The presentation will include a brief comparison between the CMAP analysis and other global precipitation analyses by other institutions. Other global precipitation analyses produced by other methodologies are also used by EMC in certain applications, such as CPC's well-known satellite-IR based technique known as "GPI", and satellite-microwave based estimates from NESDIS or NASA. Finally, the presentation will cover the three assimilation methods used by EMC to assimilate precipitation data, including 1) 3D-VAR variational assimilation in NCEP's Global Data Assimilation System (GDAS), 2) direct insertion of precipitation-inferred vertical latent heating profiles in NCEP's N. American Data Assimilation System (NDAS) and its N. American Regional Reanalysis (NARR) counterpart, and 3) direct use of observed precipitation to drive the Noah land model component of NCEP's Global and N. American Land Data Assimilation Systems (GLDAS and NLDAS). In the applications of precipitation analyses in data assimilation at NCEP, the analyses are temporally disaggregated to hourly or less using time-weights calculated from A) either radar-based estimates or an analysis of hourly gauge-observations for the CONUS-domain daily precipitation analyses, or B) global model forecasts of 6-hourly precipitation (followed by linear interpolation to hourly or less) for the global CMAP precipitation analysis.

The GEOS-iODAS: Description and Evaluation

NASA Technical Reports Server (NTRS)

Vernieres, Guillaume; Rienecker, Michele M.; Kovach, Robin; Keppenne, Christian L.

2012-01-01

This report documents the GMAO's Goddard Earth Observing System sea ice and ocean data assimilation systems (GEOS iODAS) and their evolution from the first reanalysis test, through the implementation that was used to initialize the GMAO decadal forecasts, and to the current system that is used to initialize the GMAO seasonal forecasts. The iODAS assimilates a wide range of observations into the ocean and sea ice components: in-situ temperature and salinity profiles, sea level anomalies from satellite altimetry, analyzed SST, and sea-ice concentration. The climatological sea surface salinity is used to constrain the surface salinity prior to the Argo years. Climatological temperature and salinity gridded data sets from the 2009 version of the World Ocean Atlas (WOA09) are used to help constrain the analysis in data sparse areas. The latest analysis, GEOS ODAS5.2, is diagnosed through detailed studies of the statistics of the innovations and analysis departures, comparisons with independent data, and integrated values such as volume transport. Finally, the climatologies of temperature and salinity fields from the Argo era, 2002-2011, are presented and compared with the WOA09.

Generation of an empirical soil moisture initialization and its potential impact on subseasonal forecasting skill of continental precipitation and air temperature

NASA Astrophysics Data System (ADS)

Boisserie, Marie

The goal of this dissertation research is to produce empirical soil moisture initial conditions (soil moisture analysis) and investigate its impact on the short-term (2 weeks) to subseasonal (2 months) forecasting skill of 2-m air temperature and precipitation. Because of soil moisture has a long memory and plays a role in controlling the surface water and energy budget, an accurate soil moisture analysis is today widely recognized as having the potential to increase summertime climate forecasting skill. However, because of a lack of global observations of soil moisture, there has been no scientific consensus on the importance of the contribution of a soil moisture initialization as close to the truth as possible to climate forecasting skill. In this study, the initial conditions are generated using a Precipitation Assimilation Reanalysis (PAR) technique to produce a soil moisture analysis. This technique consists mainly of nudging precipitation in the atmosphere component of a land-atmosphere model by adjusting the vertical air humidity profile based on the difference between the rate of the model-derived precipitation rate and the observed rate. The unique aspects of the PAR technique are the following: (1) based on the PAR technique, the soil moisture analysis is generated using a coupled land-atmosphere forecast model; therefore, no bias between the initial conditions and the forecast model (spinup problem) is encountered; and (2) the PAR technique is physically consistent; the surface and radiative fluxes remains in conjunction with the soil moisture analysis. To our knowledge, there has been no attempt to use a physically consistent soil moisture land assimilation system into a land-atmosphere model in a coupled mode. The effect of the PAR technique on the model soil moisture estimates is evaluated using the Global Soil Wetness Project Phase 2 (GSWP-2) multimodel analysis product (used as a proxy for global soil moisture observations) and actual in-situ observations from the state of Illinois. The results show that overall the PAR technique is effective; across most of the globe, the seasonal and anomaly variability of the model soil moisture estimates well reproduce the values of GSWP-2 in the top 1.5 m soil layer; by comparing to in-situ observations in Illinois, we find that the seasonal and anomaly soil moisture variability is also well represented deep into the soil. Therefore, in this study, we produce a new global soil moisture analysis dataset that can be used for many land surface studies (crop modeling, water resource management, soil erosion, etc.). Then, the contribution of the resulting soil moisture analysis (used as initial conditions) on air temperature and precipitation forecasts are investigated. For this, we follow the experimental set up of a model intercomparison study over the time period 1986-1995, the Global Land-Atmosphere Coupling Experiment second phase (GLACE-2), in which the FSU/COAPS climate model has participated. The results of the summertime air temperature forecasts show a significant increase in skill across most of the U.S. at short-term to subseasonal time scales. No increase in summertime precipitation forecasting skill is found at short-term to subseasonal time scales between 1986 and 1995, except for the anomalous drought year of 1988. We also analyze the forecasts of two extreme hydrological events, the 1988 U.S. drought and the 1993 U.S. flood. In general, the comparison of these two extreme hydrological event forecasts shows greater improvement for the summertime of 1988 than that of 1993, suggesting that soil moisture contributes more to the development of a drought than a flood. This result is consistent with Dirmeyer and Brubaker [1999] and Weaver et al. [2009]. By analyzing the evaporative sources of these two extreme events using the back-trajectory methodology of Dirmeyer and Brubaker [1999], we find similar results as this latter paper; the soil moisture-precipitation feedback mechanism seems to play a greater role during the drought year of 1988 than the flood year of 1993. Finally, the accuracy of this soil moisture initialization depends upon the quality of the precipitation dataset that is assimilated. Because of the lack of observed precipitation at a high temporal resolution (3-hourly) for the study period (1986-1995), a reanalysis product is used for precipitation assimilation in this study. It is important to keep in mind that precipitation data in reanalysis sometimes differ significantly from observations since precipitation is often not assimilated into the reanalysis model. In order to investigate that aspect, a similar analysis to that we performed in this study could be done using the 3-hourly Tropical Rainfall Measuring Mission (TRMM) dataset available for a the time period 1998-present. Then, since the TRMM dataset is a fully observational dataset, we expect the soil moisture initialization to be improved over that obtained in this study, which, in turn, may further increase the forecast skill.

Spatio-temporal pattern clustering for skill assessment of the Korea Operational Oceanographic System

NASA Astrophysics Data System (ADS)

Kim, J.; Park, K.

2016-12-01

In order to evaluate the performance of operational forecast models in the Korea operational oceanographic system (KOOS) which has been developed by Korea Institute of Ocean Science and Technology (KIOST), a skill assessment (SA) tool has developed and provided multiple skill metrics including not only correlation and error skills by comparing predictions and observation but also pattern clustering with numerical models, satellite, and observation. The KOOS has produced 72 hours forecast information on atmospheric and hydrodynamic forecast variables of wind, pressure, current, tide, wave, temperature, and salinity at every 12 hours per day produced by operating numerical models such as WRF, ROMS, MOM5, WW-III, and SWAN and the SA has conducted to evaluate the forecasts. We have been operationally operated several kinds of numerical models such as WRF, ROMS, MOM5, MOHID, WW-III. Quantitative assessment of operational ocean forecast model is very important to provide accurate ocean forecast information not only to general public but also to support ocean-related problems. In this work, we propose a method of pattern clustering using machine learning method and GIS-based spatial analytics to evaluate spatial distribution of numerical models and spatial observation data such as satellite and HF radar. For the clustering, we use 10 or 15 years-long reanalysis data which was computed by the KOOS, ECMWF, and HYCOM to make best matching clusters which are classified physical meaning with time variation and then we compare it with forecast data. Moreover, for evaluating current, we develop extraction method of dominant flow and apply it to hydrodynamic models and HF radar's sea surface current data. By applying pattern clustering method, it allows more accurate and effective assessment of ocean forecast models' performance by comparing not only specific observation positions which are determined by observation stations but also spatio-temporal distribution of whole model areas. We believe that our proposed method will be very useful to examine and evaluate large amount of numerical modeling data as well as satellite data.

Impact of the Assimilation of Hyperspectral Infrared Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily B.; Zavodsky, Bradley T; Jedlovec, Gary J.; Elmer, Nicholas J.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), North American Regional Reanalysis (NARR) reanalysis, and Rapid Refresh analyses.

Global Climatology of the Coastal Low-Level Wind Jets using different Reanalysis

NASA Astrophysics Data System (ADS)

Lima, Daniela C. A.; Soares, Pedro M. M.; Semedo, Alvaro; Cardoso, Rita M.

2016-04-01

Coastal Low-Level Jets (henceforth referred to as "coastal jets" or simply as CLLJ) are low-tropospheric mesoscale wind features, with wind speed maxima confined to the marine atmospheric boundary layer (MABL), typically bellow 1km. Coastal jets occur in the eastern flank of the semi-permanent subtropical mid-latitude high pressure systems, along equatorward eastern boundary currents, due to a large-scale synoptic forcing. The large-scale synoptic forcing behind CLLJ occurrences is a high pressure system over the ocean and a thermal low inland. This results in coastal parallel winds that are the consequence of the geostrophic adjustment. CLLJ are found along the California (California-Oregon) and the Canary (Iberia and Northeastern Africa) currents in the Northern Hemisphere, and along the Peru-Humboldt (Peru-Chile), Benguela (Namibia) and Western Australia (West Australia) currents in the Southern Hemisphere. In the Arabian Sea (Oman CLLJ), the interaction between the high pressure over the Indian Ocean in summer (Summer Indian Monsoon) and the Somali (also known as Findlater) Jet forces a coastal jet wind feature off the southeast coast of Oman. Coastal jets play an important role in the regional climates of the mid-latitude western continental regions. The decrease of the sea surface temperatures (SST) along the coast due to upwelling lowers the evaporation over the ocean and the coast parallel winds prevents the advection of marine air inshore. The feedback processes between the CLLJ and upwelling play a crucial role in the regional climate, namely, promoting aridity since the parallel flow prevents the intrusion of moisture inland, and increasing fish stocks through the transport of rich nutrient cold water from the bottom. In this study, the global coastal low-level wind jets are identified and characterized using an ensemble of three reanalysis, the ECMWF Interim Reanalysis (ERA-Interim), the Japanese 55-year Reanalysis (JRA-55) and the NCEP Climate Forecast System Reanalysis (NCEP CFSR). The CLLJ detection method proposed by Ranjha et al. (2013) was used for the reanalysis data. The criteria was applied sequentially to wind-speed and temperature vertical profiles to detect the location and frequency of CLLJ. The CLLJs spatio-temporal features and the seasonal synoptic configuration associated with the presence of coastal jets are studied for the period (1979-2008) using the ensemble. The present study will allow us to investigate thoroughly the global coastal low-level jets occurrence and main properties, following a new perspective and to assess the uncertainties in the representation of this jets by the available reanalysis. ublication supported by project FCT UID/GEO/50019/2013 - Instituto Dom Luiz.

Retrospective evaluation of continental-scale streamflow nudging with WRF-Hydro National Water Model V1

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Wu, Y.; Gochis, D.; Rafieeinasab, A.; Dugger, A. L.; Yu, W.; Cosgrove, B.; Cui, Z.; Oubeidillah, A.; Briar, D.

2016-12-01

The streamflow (discharge) data assimilation capability in version 1 of the National Water Model (NWM; a WRF-Hydro configuration) is applied and evaluated in a 5-year (2011-2015) retrospective study using NLDAS2 forcing data over CONUS. This talk will describe the NWM V1 operational nudging (continuous-time) streamflow data assimilation approach, its motivation, and its relationship to this retrospective evaluation. Results from this study will provide a an analysis-based (not forecast-based) benchmark for streamflow DA in the NWM. The goal of the assimilation is to reduce discharge bias and improve channel initial conditions for discharge forecasting (though forecasts are not considered here). The nudging method assimilates discharge observations at nearly 7,000 USGS gages (at frequency up to 1/15 minutes) to produce a (univariate) discharge reanalysis (i.e. this is the only variable affected by the assimilation). By withholding 14% nested gages throughout CONUS in a separate validation run, we evaluate the downstream impact of assimilation at upstream gages. Based on this sample, we estimate the skill of the streamflow reanalysis at ungaged locations and examine factors governing the skill of the assimilation. Comparison of assimilation and open-loop runs is presented. Performance of DA under both high and low flow regimes and selected flooding events is examined. Preliminary evaluation of nudging parameter sensitivity and its relationship to flow regime will be presented.

Application of web-GIS approach for climate change study

NASA Astrophysics Data System (ADS)

Okladnikov, Igor; Gordov, Evgeny; Titov, Alexander; Bogomolov, Vasily; Martynova, Yuliya; Shulgina, Tamara

2013-04-01

Georeferenced datasets are currently actively used in numerous applications including modeling, interpretation and forecast of climatic and ecosystem changes for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their huge size which might constitute up to tens terabytes for a single dataset at present studies in the area of climate and environmental change require a special software support. A dedicated web-GIS information-computational system for analysis of georeferenced climatological and meteorological data has been created. It is based on OGC standards and involves many modern solutions such as object-oriented programming model, modular composition, and JavaScript libraries based on GeoExt library, ExtJS Framework and OpenLayers software. The main advantage of the system lies in a possibility to perform mathematical and statistical data analysis, graphical visualization of results with GIS-functionality, and to prepare binary output files with just only a modern graphical web-browser installed on a common desktop computer connected to Internet. Several geophysical datasets represented by two editions of NCEP/NCAR Reanalysis, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis, ECMWF ERA Interim Reanalysis, MRI/JMA APHRODITE's Water Resources Project Reanalysis, DWD Global Precipitation Climatology Centre's data, GMAO Modern Era-Retrospective analysis for Research and Applications, meteorological observational data for the territory of the former USSR for the 20th century, results of modeling by global and regional climatological models, and others are available for processing by the system. And this list is extending. Also a functionality to run WRF and "Planet simulator" models was implemented in the system. Due to many preset parameters and limited time and spatial ranges set in the system these models have low computational power requirements and could be used in educational workflow for better understanding of basic climatological and meteorological processes. The Web-GIS information-computational system for geophysical data analysis provides specialists involved into multidisciplinary research projects with reliable and practical instruments for complex analysis of climate and ecosystems changes on global and regional scales. Using it even unskilled user without specific knowledge can perform computational processing and visualization of large meteorological, climatological and satellite monitoring datasets through unified web-interface in a common graphical web-browser. This work is partially supported by the Ministry of education and science of the Russian Federation (contract #8345), SB RAS project VIII.80.2.1, RFBR grant #11-05-01190a, and integrated project SB RAS #131.

Development of WRF-ROI system by incorporating eigen-decomposition

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Song, H.; Lim, G.

2011-12-01

This study presents the development of WRF-ROI system, which is the implementation of Retrospective Optimal Interpolation (ROI) to the Weather Research and Forecasting model (WRF). ROI is a new data assimilation algorithm introduced by Song et al. (2009) and Song and Lim (2009). The formulation of ROI is similar with that of Optimal Interpolation (OI), but ROI iteratively assimilates an observation set at a post analysis time into a prior analysis, possibly providing the high quality reanalysis data. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In previous study, ROI method is applied to Lorenz 40-variable model (Lorenz, 1996) to validate the algorithm and to investigate the capability. It is therefore required to apply this ROI method into a more realistic and complicated model framework such as WRF. In this research, the reduced-rank formulation of ROI is used instead of a reduced-resolution method. The computational costs can be reduced due to the eigen-decomposition of background error covariance in the reduced-rank method. When single profile of observations is assimilated in the WRF-ROI system by incorporating eigen-decomposition, the analysis error tends to be reduced if compared with the background error. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation.

Monitoring and seasonal forecasting of meteorological droughts

NASA Astrophysics Data System (ADS)

Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian

2015-04-01

Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. Unfortunately, a major constraint in current drought outlooks is the lack of reliable monitoring capability for observed precipitation globally in near-real time. Furthermore, drought monitoring systems requires a long record of past observations to provide mean climatological conditions. We address these constraints by developing a novel drought monitoring approach in which monthly mean precipitation is derived from short-range using ECMWF probabilistic forecasts and then merged with the long term precipitation climatology of the Global Precipitation Climatology Centre (GPCC) dataset. Merging the two makes available a real-time global precipitation product out of which the Standardized Precipitation Index (SPI) can be estimated and used for global or regional drought monitoring work. This approach provides stability in that by-passes problems of latency (lags) in having local rain-gauge measurements available in real time or lags in satellite precipitation products. Seasonal drought forecasts can also be prepared using the common methodology and based upon two data sources used to provide initial conditions (GPCC and the ECMWF ERA-Interim reanalysis (ERAI) combined with either the current ECMWF seasonal forecast or a climatology based upon ensemble forecasts. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar to or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. However, results presented regionally and globally pinpoint several regions in the world where drought onset forecasting is feasible and skilful.

Application of a GCM Ensemble Seasonal Climate Forecasts to Crop Yield Prediction in East Africa

NASA Astrophysics Data System (ADS)

Ogutu, G.; Franssen, W.; Supit, I.; Hutjes, R. W. A.

2016-12-01

We evaluated the potential use of ECMWF System-4 seasonal climate forecasts (S4) for impacts analysis over East Africa. Using the 15 member, 7 months ensemble forecasts initiated every month for 1981-2010, we tested precipitation (tp), air temperature (tas) and surface shortwave radiation (rsds) forecast skill against the WATCH forcing Data ERA-Interim (WFDEI) re-analysis and other data. We used these forecasts as input in the WOFOST crop model to predict maize yields. Forecast skill is assessed using anomaly correlation (ACC), Ranked Probability Skill Score (RPSS) and the Relative Operating Curve Skill Score (ROCSS) for MAM, JJA and OND growing seasons. Predicted maize yields (S4-yields) are verified against historical observed FAO and nationally reported (NAT) yield statistics, and yields from the same crop model forced by WFDEI (WFDEI-yields). Predictability of the climate forecasts vary with season, location and lead-time. The OND tp forecasts show skill over a larger area up to three months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20-80% better than climatology. Good tas forecast skill is apparent with three months lead-time. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher against others. S4-forecasts captures ENSO related anomalous years with region dependent skill. Anomalous ENSO influence is also seen in simulated yields. Focussing on the main sowing dates in the northern (July), equatorial (March-April) and southern (December) regions, WFDEI-yields are lower than FAO and NAT but anomalies are comparable. Yield anomalies are predictable 3-months before sowing in most of the regions. Differences in interannual variability in the range of ±40% may be related to sensitivity of WOFOST to drought stress while the ACCs are largely positive ranging from 0.3 to 0.6. Above and below-normal yields are predictable with 2-months lead time. We evidenced a potential use of seasonal climate forecasts with a crop simulation model to predict anomalous maize yields over East Africa. The findings open a window to better use of climate forecasts in food security early warning systems, and pre-season policy and farm management decisions.

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

NASA Astrophysics Data System (ADS)

Shrivastava, Sourabh; Kar, Sarat C.; Sharma, Anu Rani

2017-07-01

Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002-2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1 m3/m3 compared to climatological values of more than 0.35 m3/m3. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.

Diagnostic Comparison of Meteorological Analyses during the 2002 Antarctic Winter

NASA Technical Reports Server (NTRS)

Manney, Gloria L.; Allen, Douglas R.; Kruger, Kirstin; Naujokat, Barbara; Santee, Michelle L.; Sabutis, Joseph L.; Pawson, Steven; Swinbank, Richard; Randall, Cora E.; Simmons, Adrian J.;

Development of web-GIS system for analysis of georeferenced geophysical data

NASA Astrophysics Data System (ADS)

Okladnikov, I.; Gordov, E. P.; Titov, A. G.; Bogomolov, V. Y.; Genina, E.; Martynova, Y.; Shulgina, T. M.

2012-12-01

Georeferenced datasets (meteorological databases, modeling and reanalysis results, remote sensing products, etc.) are currently actively used in numerous applications including modeling, interpretation and forecast of climatic and ecosystem changes for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their huge size which might constitute up to tens terabytes for a single dataset at present studies in the area of climate and environmental change require a special software support. A dedicated web-GIS information-computational system for analysis of georeferenced climatological and meteorological data has been created. The information-computational system consists of 4 basic parts: computational kernel developed using GNU Data Language (GDL), a set of PHP-controllers run within specialized web-portal, JavaScript class libraries for development of typical components of web mapping application graphical user interface (GUI) based on AJAX technology, and an archive of geophysical datasets. Computational kernel comprises of a number of dedicated modules for querying and extraction of data, mathematical and statistical data analysis, visualization, and preparing output files in geoTIFF and netCDF format containing processing results. Specialized web-portal consists of a web-server Apache, complying OGC standards Geoserver software which is used as a base for presenting cartographical information over the Web, and a set of PHP-controllers implementing web-mapping application logic and governing computational kernel. JavaScript libraries aiming at graphical user interface development are based on GeoExt library combining ExtJS Framework and OpenLayers software. The archive of geophysical data consists of a number of structured environmental datasets represented by data files in netCDF, HDF, GRIB, ESRI Shapefile formats. For processing by the system are available: two editions of NCEP/NCAR Reanalysis, JMA/CRIEPI JRA-25 Reanalysis, ECMWF ERA-40 Reanalysis, ECMWF ERA Interim Reanalysis, MRI/JMA APHRODITE's Water Resources Project Reanalysis, DWD Global Precipitation Climatology Centre's data, GMAO Modern Era-Retrospective analysis for Research and Applications, meteorological observational data for the territory of the former USSR for the 20th century, results of modeling by global and regional climatological models, and others. The system is already involved into a scientific research process. Particularly, recently the system was successfully used for analysis of Siberia climate changes and its impact in the region. The Web-GIS information-computational system for geophysical data analysis provides specialists involved into multidisciplinary research projects with reliable and practical instruments for complex analysis of climate and ecosystems changes on global and regional scales. Using it even unskilled user without specific knowledge can perform computational processing and visualization of large meteorological, climatological and satellite monitoring datasets through unified web-interface in a common graphical web-browser. This work is partially supported by the Ministry of education and science of the Russian Federation (contract #07.514.114044), projects IV.31.1.5, IV.31.2.7, RFBR grants #10-07-00547a, #11-05-01190a, and integrated project SB RAS #131.

Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States Climate Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 x 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmentalmore » Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation’s performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.« less

Biases in Global Reanalysis Datasets Undermine Intraseasonal Prediction Skill Xiouhua Fu1, Bin Wang, June-Yi Lee, Wanqiu Wang, and Li Gao 1International Pacific Research Center (IPRC), SOEST, University of Hawaii at Manoa

NASA Astrophysics Data System (ADS)

Fu, J. X.

2010-12-01

Predictability of Intra-Seasonal Oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere-ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets (NCEP_R1, _R2, and ERA_Interim) in describing ISO were revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal recovery method is proposed to improve ISO prediction. All three reanalysis datasets underestimate the intensity of the equatorial eastward-propagating ISO. When these reanalyses are used as initial conditions in the ECHAM4-UH hybrid coupled model (UH_HCM hereinafter), skillful ISO prediction reaches only about one week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2-3 days and U850 prediction by 5-10 days. After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength much closer to the observed. Use of these signal-recovered reanalyses as initial conditions extends the skillful prediction of U850 and rainfall, respectively, to 23 and 18 days over Southeast Asia, and to 20 and 10 days over the global tropics. This finding underlines the urgent need to improve data assimilation systems and observations in advancement of ISO prediction by offering better initial conditions. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skill. The UH_HCM has better rainfall prediction than the NCEP Climate Forecast System (CFS) over Southeast Asia and both models suffer the prediction barrier over the Maritime Continent.

CWRF performance at downscaling China climate characteristics

NASA Astrophysics Data System (ADS)

Liang, Xin-Zhong; Sun, Chao; Zheng, Xiaohui; Dai, Yongjiu; Xu, Min; Choi, Hyun I.; Ling, Tiejun; Qiao, Fengxue; Kong, Xianghui; Bi, Xunqiang; Song, Lianchun; Wang, Fang

2018-05-01

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities.

Toward a multivariate reanalysis of the North Atlantic ocean biogeochemistry during 1998-2006 based on the assimilation of SeaWiFS chlorophyll data

NASA Astrophysics Data System (ADS)

Fontana, C.; Brasseur, P.; Brankart, J.-M.

2012-04-01

Today, the routine assimilation of satellite data into operational models of the ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical-biogeochemical model of the North-Atlantic. The aim is on one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical-biogeochemical modelling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9 year-long period. In this frame, two experiences are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, the nitrate World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface chlorophyll concentrations analysis and forecast, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related litterature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentration deeper than 50 m. The assessement of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalysing the ocean biogeochemistry based on ocean color data.

Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998-2006 based on the assimilation of SeaWiFS chlorophyll data

NASA Astrophysics Data System (ADS)

Fontana, C.; Brasseur, P.; Brankart, J.-M.

2013-01-01

Today, the routine assimilation of satellite data into operational models of ocean circulation is mature enough to enable the production of global reanalyses describing the ocean circulation variability during the past decades. The expansion of the "reanalysis" concept from ocean physics to biogeochemistry is a timely challenge that motivates the present study. The objective of this paper is to investigate the potential benefits of assimilating satellite-estimated chlorophyll data into a basin-scale three-dimensional coupled physical-biogeochemical model of the North Atlantic. The aim is on the one hand to improve forecasts of ocean biogeochemical properties and on the other hand to define a methodology for producing data-driven climatologies based on coupled physical-biogeochemical modeling. A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9-year period. In this frame, two experiments are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, nitrate of the World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface analysis and forecast chlorophyll concentrations, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related literature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentrations deeper than 50 meters. The assessment of the non-linear assimilation experiments indicates that the proposed methodology provides the skeleton of an assimilative system suitable for reanalyzing the ocean biogeochemistry based on ocean color data.

The Mediterranean Forecasting System: recent developments

NASA Astrophysics Data System (ADS)

Tonani, Marina; Oddo, Paolo; Korres, Gerasimos; Clementi, Emanuela; Dobricic, Srdjan; Drudi, Massimiliano; Pistoia, Jenny; Guarnieri, Antonio; Romaniello, Vito; Girardi, Giacomo; Grandi, Alessandro; Bonaduce, Antonio; Pinardi, Nadia

2014-05-01

Recent developments of the Mediterranean Monitoring and Forecasting Centre of the EU-Copernicus marine service, the Mediterranean Forecasting System (MFS), are presented. MFS provides forecast, analysis and reanalysis for the physical and biogeochemical parameters of the Mediterranean Sea. The different components of the system are continuously updated in order to provide to the users the best available product. This work is focus on the physical component of the system. The physical core of MFS is composed by an ocean general circulation model (NEMO) coupled with a spectral wave model (Wave Watch-III). The NEMO model provides to WW-III surface currents and SST fields, while WW-III returns back to NEMO the neutral component of the surface drag coefficient. Satellite Sea Level Anomaly observations and in-situ T & S vertical profiles are assimilated into this system using a variational assimilation scheme based on 3DVAR (Dobricic, 2008) . Sensitive experiments have been performed in order to assess the impact of the assimilation of the latest available SLA missions, Altika and Cryosat together with the long term available mission of Jason2. The results show a significant improvement of the MFS skill due to the multi-mission along track assimilation. The primitive equations module has been recently upgraded with the introduction of the atmospheric pressure term and a new, explicit, numerical scheme has been adopted to solve the barotropic component of the equations of motion. The SLA satellite observations for data assimilation have been consequently modified in order to account for the new atmospheric pressure term introduced in the equations. This new system has been evaluated using tide gauge coastal buoys and the satellite along track data. The quality of the SSH has improved significantly while a minor impact has been observed on the other state variables (temperature, salinity and currents). Experiments with a higher resolution NWP (numerical weather prediction) forcing provided by the COSMO-MED system (provided by the Italian Meteorological Office), have been performed and a pre-operational 3-day forecast production system has been developed. The comparison between this system and the official one forced by the ECMWF NWP data will be discussed.

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

NASA Astrophysics Data System (ADS)

Li, Tao; Zheng, Xiaogu; Dai, Yongjiu; Yang, Chi; Chen, Zhuoqi; Zhang, Shupeng; Wu, Guocan; Wang, Zhonglei; Huang, Chengcheng; Shen, Yan; Liao, Rongwei

2014-09-01

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Preliminary results and assessment of the MAR outputs over High Mountain Asia

NASA Astrophysics Data System (ADS)

Linares, M.; Tedesco, M.; Margulis, S. A.; Cortés, G.; Fettweis, X.

2017-12-01

Lack of ground measurements has made the use of regional climate models (RCMs) over the High Mountain Asia (HMA) pivotal for understanding the impact of climate change on the hydrological cycle and on the cryosphere. Here, we show an analysis of the assessment of the outputs of Modèle Atmosphérique Régionale (MAR) model RCM over the HMA region as part of the NASA-funded project `Understanding and forecasting changes in High Mountain Asia snow hydrology via a novel Bayesian reanalysis and modeling approach'. The first step was to evaluate the impact of the different forcings on MAR outputs. To this aim, we performed simulations for the 2007 - 2008 and 2014 - 2015 years forcing MAR at its boundaries either with reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) or from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The comparison between the outputs obtained with the two forcings indicates that the impact on MAR simulations depends on specific parameters. For example, in case of surface pressure the maximum percentage error is 0.09 % while the 2-m air temperature has a maximum percentage error of 103.7%. Next, we compared the MAR outputs with reanalysis data fields over the region of interest. In particular, we evaluated the following parameters: surface pressure, snow depth, total cloud cover, two meter temperature, horizontal wind speed, vertical wind speed, wind speed, surface new solar radiation, skin temperature, surface sensible heat flux, and surface latent heat flux. Lastly, we report results concerning the assessment of MAR surface albedo and surface temperature over the region through MODIS remote sensing products. Next steps are to determine whether RCMs and reanalysis datasets are effective at capturing snow and snowmelt runoff processes in the HMA region through a comparison with in situ datasets. This will help determine what refinements are necessary to improve RCM outputs.

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

NASA Technical Reports Server (NTRS)

Rokke, L.; Joiner, J.

1999-01-01

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

The neglected Indo-Gangetic Plains low-level jet and its importance for moisture transport and precipitation during the peak summer monsoon

NASA Astrophysics Data System (ADS)

Acosta, R. P.; Huber, M.

2017-08-01

Accurately simulating the Indo-Asian monsoon (IAM) using atmospheric general circulation models (AGCMs) is challenging but crucial. This study uses reanalysis products European Centre of Medium-Range Forecast Interim reanalysis, Japanese Reanalysis year 55, and High Asia Reanalysis to highlight an easterly, low-level barrier jet along the Indo-Gangetic Plain (referred from here as IG LLJ), which we identify as the primary moisture transport mechanism for the northeastern branch of the IAM. We show that the NCAR family of AGCMs (Community Atmospheric Model (CAM)) does not capture this circulation until 1/2° or greater spatial horizontal resolution is used. The IG LLJ develops due to a persistent low-pressure system centered over the Ganges basin and is enhanced by the Himalayas. Using diabatic heating rates and the moist Froude number as diagnostics, we find that in CAM, this branch of the IAM displays two different dynamical regimes as a function of resolution. At low resolution, the atmosphere near the Himalayas is statically unstable, diabatic heating is strong, and the moisture flow is southwesterly from the Arabian Sea and moves over the terrain (unblocked). At high resolution, the moist static stability near the Himalayan Mountains is stable, diabatic heating is weak, and the flow primarily enters easterly from the Bay of Bengal and moves parallel to the terrain (blocked). During the summer season, the low-resolution CAM is locked into the unblocked mode, which has serious implications for interpreting topography-monsoon interactions. For a broader context, we demonstrate that more than half of the CMIP5 models do not capture the IG LLJ, which further highlights model-data mismatch across the IAM region.

A global validation of ERA-Interim integrated water vapor estimates using ground-based GNSS observations

NASA Astrophysics Data System (ADS)

Ahmed, F.; Dousa, J.; Hunegnaw, A.; Teferle, F. N.; Bingley, R.

2017-12-01

Integrated water vapor (IWV) derived from climate reanalysis models, such as the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis-Interim (ERA-Interim), is widely used in many atmospheric applications. Therefore, it is of interest to assess the quality of this reanalysis product using available observations. Observations from Global Navigation Satellite Systems (GNSS) are, as of now, available for a period of over 2 decades and their global availability makes it possible to validate the IWV obtained from climate reanalysis models in different geographical and climatic regions. In this study, primarily, three 5-year long homogeneously reprocessed GNSS-derived IWV datasets containing over 400 globally distributed ground-based GNSS stations have been used to validate the IWV estimates obtained from the ERA-Interim climate reanalysis model in 25 different climate zones. The IWV from ERA-Interim has been obtained by vertically integrating the specific humidity at all model levels above the locations of GNSS stations. It has been studied how the difference between the ERA-Interim IWV and the GNSS-derived IWV varies with respect to the different climate zones as well as with respect to the difference in the model orography and latitude. The results show a dependence of the ability of ERA-Interim to model the IWV on difference in climate types and latitude. This dependence, however, is dictated by the concentration of water vapor in different climate zones and at different latitudes. Furthermore, as a secondary focus of this study, the weighted mean atmospheric temperature (Tm) obtained from ERA-Interim has been compared to its equivalent obtained using two widely used approximations globally.

REMO poor man's reanalysis

NASA Astrophysics Data System (ADS)

Ries, H.; Moseley, C.; Haensler, A.

2012-04-01

Reanalyses depict the state of the atmosphere as a best fit in space and time of many atmospheric observations in a physically consistent way. By essentially solving the data assimilation problem in a very accurate manner, reanalysis results can be used as reference for model evaluation procedures and as forcing data sets for different model applications. However, the spatial resolution of the most common and accepted reanalysis data sets (e.g. JRA25, ERA-Interim) ranges from approximately 124 km to 80 km. This resolution is too coarse to simulate certain small scale processes often associated with extreme events. In addition, many models need higher resolved forcing data ( e.g. land-surface models, tools for identifying and assessing hydrological extremes). Therefore we downscaled the ERA-Interim reanalysis over the EURO-CORDEX-Domain for the time period 1989 to 2008 to a horizontal resolution of approximately 12 km. The downscaling is performed by nudging REMO-simulations to lower and lateral boundary conditions of the reanalysis, and by re-initializing the model every 24 hours ("REMO in forecast mode"). In this study the three following questions will be addressed: 1.) Does the REMO poor man's reanalysis meet the needs (accuracy, extreme value distribution) in validation and forcing? 2.) What lessons can be learned about the model used for downscaling? As REMO is used as a pure downscaling procedure, any systematic deviations from ERA-Interim result from poor process modelling but not from predictability limitations. 3.) How much small scale information generated by the downscaling model is lost with frequent initializations? A comparison to a simulation that is performed in climate mode will be presented.

Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

NASA Astrophysics Data System (ADS)

WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

2016-12-01

The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different stations. Further analysis shows that this advantage of LIM is likely to arise from its representation of local zonal winds and the position of Intertropical Convergence Zone (ITCZ).

Stratospheric water vapor and ozone evaluation in reanalyses as part of the SPARC Reanalysis Intercomparison Project (S-RIP)

NASA Astrophysics Data System (ADS)

Davis, S. M.; Hegglin, M. I.; Fujiwara, M.; Manney, G. L.; Dragani, R.; Nash, E.; Tegtmeier, S.; Kobayashi, C.; Harada, Y.; Long, C. S.; Wargan, K.; Rosenlof, K. H.

2017-12-01

Reanalyses are widely used to understand atmospheric processes and past variability, and are often used to stand in as "observations" for comparisons with climate model output. Because of the central role of water vapor (WV) and ozone (O3) in climate change, it is important to understand how accurately and consistently these species are represented in existing global reanalyses. Here we present the results of WV and O3 intercomparisons that have been performed as part of the SPARC (Stratosphere-troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The comparisons cover a range of timescales and evaluate both inter-reanalysis and observation-reanalysis differences. The assimilation of total column ozone (TCO) observations in newer reanalyses results in realistic representations of TCO in reanalyses except when data coverage is lacking, such as during polar night. The vertical distribution of ozone is also relatively well represented in the stratosphere in reanalyses, particularly given the relatively weak constraints on ozone vertical structure provided by most assimilated observations and the simplistic representations of ozone photochemical processes in most of the reanalysis forecast models. For times when vertically resolved observations are not assimilated, biases in the vertical distribution of ozone are found in the upper troposphere and lower stratosphere in all reanalyses. In contrast to O3, reanalysis stratospheric WV fields are not directly constrained by assimilated data. Observations of atmospheric humidity are typically used only in the troposphere, below a specified vertical level at or near the tropopause. The fidelity of reanalysis stratospheric WV products is therefore dependent on the reanalyses' representation of processes that influence stratospheric WV, such as tropical tropopause layer temperatures and methane oxidation. The lack of assimilated observations and known deficiencies in the representation of stratospheric transport in reanalyses result in much poorer agreement amongst observational and reanalysis estimates of stratospheric WV. Hence, stratospheric WV products from the current generation of reanalyses should generally not be used in scientific studies.

Optimising predictor domains for spatially coherent precipitation downscaling

NASA Astrophysics Data System (ADS)

Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.

2012-04-01

Relationships between local precipitation (predictands) and large-scale circulation (predictors) are used for statistical downscaling purposes in various contexts, from medium-term forecasting to climate change impact studies. For hydrological purposes like flood forecasting, the downscaled precipitation spatial fields have furthermore to be coherent over possibly large basins. This thus first requires to know what predictor domain can be associated to the precipitation over each part of the studied basin. This study addresses this issue by identifying the optimum predictor domains over the whole of France, for a specific downscaling method based on a analogue approach and developed by Ben Daoud et al. (2011). The downscaling method used here is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. The optimum predictor domain has been found to consist of the nearest grid cell for all variables except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have been found to be sensitive to the target location by Obled et al. (2002), and the present study thus focuses on optimizing the domains of this specific predictor over France. The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608 climatologically homogeneous zones in France using the ERA-40 reanalysis data for the large-scale predictors and local precipitation from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25 best analogue days found with the statistical downscaling method. Rectangular predictor domains of different sizes, shapes and locations are tested, and the one that leads to the smallest CRPS for the zone in question is retained. The resulting optimised domains are analysed for defining regions where neighbouring zones have equal or similar predictor domains and identifying which French river basins contain zones associated with different predictor domains, i.e. are exposed to different meteorological influences. The above analysis will be used (1) to extend the statistical downscaling method of Ben Daoud et al. (2011) to the whole of France and (2) to develop it further in order to achieve spatially coherent forecasts while preserving the predictive skill on the local scale. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Obled, C., Bontron, G., and Garçon, R. (2002). Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach. Atmospheric Research, 63(3-4):303-324. doi: 10.1016/S0169-8095(02)00038-8 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003

A High-Resolution WRF Tropical Channel Simulation Driven by a Global Reanalysis

NASA Astrophysics Data System (ADS)

Holland, G.; Leung, L.; Kuo, Y.; Hurrell, J.

2006-12-01

Since 2003, NCAR has invested in the development and application of Nested Regional Climate Model (NRCM) based on the Weather Research and Forecasting (WRF) model and the Community Climate System Model, as a key component of the Prediction Across Scales Initiative. A prototype tropical channel model has been developed to investigate scale interactions and the influence of tropical convection on large scale circulation and tropical modes. The model was developed based on the NCAR Weather Research and Forecasting Model (WRF), configured as a tropical channel between 30 ° S and 45 ° N, wide enough to allow teleconnection effects over the mid-latitudes. Compared to the limited area domain that WRF is typically applied over, the channel mode alleviates issues with reflection of tropical modes that could result from imposing east/west boundaries. Using a large amount of available computing resources on a supercomputer (Blue Vista) during its bedding in period, a simulation has been completed with the tropical channel applied at 36 km horizontal resolution for 5 years from 1996 to 2000, with large scale circulation provided by the NCEP/NCAR global reanalysis at the north/south boundaries. Shorter simulations of 2 years and 6 months have also been performed to include two-way nests at 12 km and 4 km resolution, respectively, over the western Pacific warm pool, to explicitly resolve tropical convection in the Maritime Continent. The simulations realistically captured the large-scale circulation including the trade winds over the tropical Pacific and Atlantic, the Australian and Asian monsoon circulation, and hurricane statistics. Preliminary analysis and evaluation of the simulations will be presented.

High-resolution regional climate simulations of precipitation and snowpack over the US northern Rockies in a changing climate

NASA Astrophysics Data System (ADS)

Wang, Y.; Geerts, B.; Liu, C.

2015-12-01

This work first examines the performance of a regional climate model in capturing orographic precipitation and snowpack dynamics in the northern US Rockies. The Weather Research and Forecasting (WRF) model is run at a sufficiently fine resolution (4-km horizontal grid spacing), over a sub-continental domain driven by the Climate Forecast System Reanalysis (CFSR), to examine WRF's ability to simulate the observed seasonal precipitation and snowpack dynamics. WRF retrospective simulations are being run over a 30-year period from 1980 to 2010. Observations from Snow Telemetry (SNOTEL, providing precipitation rate and snowpack snow water equivalent (SWE)) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM, providing fine-scale monthly mean values of precipitation and temperature) are used for validation. The results show that WRF captures observed seasonal precipitation and snowpack build-up reasonably well. The second part of this work is in progress. A pseudo-global warming (PGW) technique is used to perturb the retrospective reanalysis with the anticipated change according to the consensus global model guidance under the CMIP5 "high emissions" (RCP8.5) scenario produced by the CCSM4. This technique preserves low-frequency general circulation patterns and the characteristics of storms entering the domain. The WRF model is rerun over 30 years centered on 2050 with perturbed initial and boundary conditions. The results will be used to examine the effect of climate variability and projected global warming on the statistical distributions of precipitation amounts and SWE in the studied domain.

High-resolution dynamical downscaling of re-analysis data over the Kerguelen Islands using the WRF model

NASA Astrophysics Data System (ADS)

Fonseca, Ricardo; Martín-Torres, Javier

2018-03-01

We have used the Weather Research and Forecasting (WRF) model to simulate the climate of the Kerguelen Islands (49° S, 69° E) and investigate its inter-annual variability. Here, we have dynamically downscaled 30 years of the Climate Forecast System Reanalysis (CFSR) over these islands at 3-km horizontal resolution. The model output is found to agree well with the station and radiosonde data at the Port-aux-Français station, the only location in the islands for which observational data is available. An analysis of the seasonal mean WRF data showed a general increase in precipitation and decrease in temperature with elevation. The largest seasonal rainfall amounts occur at the highest elevations of the Cook Ice Cap in winter where the summer mean temperature is around 0 °C. Five modes of variability are considered: conventional and Modoki El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Subtropical IOD (SIOD) and Southern Annular Mode (SAM). It is concluded that a key mechanism by which these modes impact the local climate is through interaction with the diurnal cycle in particular in the summer season when it has a larger magnitude. One of the most affected regions is the area just to the east of the Cook Ice Cap extending into the lower elevations between the Gallieni and Courbet Peninsulas. The WRF simulation shows that despite the small annual variability, the atmospheric flow in the Kerguelen Islands is rather complex which may also be the case for the other islands located in the Southern Hemisphere at similar latitudes.

Tropical cyclones in the North American Regional Reanalysis: The impact of satellite-derived precipitation over ocean

NASA Astrophysics Data System (ADS)

Zick, Stephanie E.; Matyas, Corene J.

2015-09-01

Continued advancement in the realm of tropical cyclone (TC) forecasting requires a more accurate depiction of these storms at model initialization. This study examines the impact of precipitation assimilation on the representation of TCs in the North American Regional Reanalysis before and after the 2004 introduction of precipitation assimilation over ocean in the vicinity of TCs. The probability distribution function of rainfall rates indicates that light (heavy) precipitation was overforecast (underforecast) in the early time period. Since the precipitation assimilation is applied through an adjustment to the latent heating distribution, the data assimilation system in the later time period initializes a low-level moisture and heating profile that is more conducive to the initiation of deep convection and the generation of precipitation. Consequently, the deep convection and enhanced latent heat release lead to a more robust warm-core temperature perturbation and a better developed secondary circulation, which supplies the TC with larger quantities of moisture from the large-scale environment. Furthermore, the evolution of TC size, which was objectively estimated though the radius of outermost closed isobar, is significantly more skillful (p < 0.05) in post-2003 storms. Based on this study, precipitation assimilation leads to a better analysis of temperature, winds, and moisture in the vicinity of TCs, resulting in improved representations of the water budget and storm life cycle. Therefore, we conclude that efforts toward the development of precipitation assimilation techniques from radar and satellite data sets will be valuable toward the construction of improved TC forecasting tools with more authentic TC representation.

A Coastal Bay Summer Breeze Study, Part 2: High-resolution Numerical Simulation of Sea-breeze Local Influences

NASA Astrophysics Data System (ADS)

Calmet, Isabelle; Mestayer, Patrice G.; van Eijk, Alexander M. J.; Herlédant, Olivier

2018-04-01

We complete the analysis of the data obtained during the experimental campaign around the semi circular bay of Quiberon, France, during two weeks in June 2006 (see Part 1). A reanalysis of numerical simulations performed with the Advanced Regional Prediction System model is presented. Three nested computational domains with increasing horizontal resolution down to 100 m, and a vertical resolution of 10 m at the lowest level, are used to reproduce the local-scale variations of the breeze close to the water surface of the bay. The Weather Research and Forecasting mesoscale model is used to assimilate the meteorological data. Comparisons of the simulations with the experimental data obtained at three sites reveal a good agreement of the flow over the bay and around the Quiberon peninsula during the daytime periods of sea-breeze development and weakening. In conditions of offshore synoptic flow, the simulations demonstrate that the semi-circular shape of the bay induces a corresponding circular shape in the offshore zones of stagnant flow preceding the sea-breeze onset, which move further offshore thereafter. The higher-resolution simulations are successful in reproducing the small-scale impacts of the peninsula and local coasts (breeze deviations, wakes, flow divergences), and in demonstrating the complexity of the breeze fields close to the surface over the bay. Our reanalysis also provides guidance for numerical simulation strategies for analyzing the structure and evolution of the near-surface breeze over a semi-circular bay, and for forecasting important flow details for use in upcoming sailing competitions.

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

Global reanalyses over Antarctica and the Southern Ocean: Can they be used prior to 1979?

NASA Astrophysics Data System (ADS)

Bromwich, D. H.; Nicolas, J. P.

2017-12-01

High southern latitudes are a notoriously challenging area for global reanalyses, largely due to the scarcity of conventional observations in these regions. This lack of observational constraint not only reduces the reanalysis model forecast skill, but is also responsible for artifacts in their time series tied to changes in the observing system. For example, the introduction of new satellite observations (e.g., AMSU in 1998) is now a well-documented cause of widespread spurious changes in the reanalysis moisture and temperature fields, which are often exacerbated over Antarctica and the Southern Ocean. This lack of temporal consistency has significantly reduced the reliability of some reanalysis products and their suitability for trend analysis. Century-long reanalysis efforts such as 20CR and ERA-20C, which only assimilate surface pressure observations, have provided ways to achieve greater homogeneity in the observing system through time and (potentially) produce more temporally consistent datasets, particularly across 1979 and the onset of the modern satellite era. However, important issues quickly became apparent in these reanalyses, related in particular to the handling by their data assimilation systems of the near-complete absence of observations poleward of 50°S prior to the 1950s, or to the prescription of ocean boundary conditions (sea ice, SST) prior to 1979. Because of the data scarcity, comparing reanalyses with each other is one of the primary means to assess their reliability. As such, the release of the CERA-20C and ERA5 (partially) by ECMWF in 2017 provides an opportunity to reassess the skill of recent global reanalyses in high southern latitudes and take stock of the recent improvements and remaining challenges, particularly with regard to their use for long-term climate change studies. Our comparison will include both satellite-era comprehensive reanalyses (ERA-Interim, CFSR, MERRA2, JRA-55, and ERA5) and century-long limited reanalyses (20CR, ERA-20C, and CERA-20C). The focus will be placed on key climate variables such as sea level pressure, near-surface temperature, and precipitation.

Forecasting and Reanalysis in the Monterey Bay/California Current Region for the Autonomous Ocean Sampling Network-II Experiment

DTIC Science & Technology

2008-03-26

only after shifting the GDEM 2 climatology (Davis et al., 1986) to the observed AOSN-II mean that the larger-scale Off Shore Domain was of some use...T.M., K.A. Countryman and M.J. Carron (1986) Tailored acoustic products utilizing the NAVOCEANO GDEM (a generalized digital envi- ronmental model). in

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Astrophysics Data System (ADS)

Molthan, A.; Limaye, A. S.

2011-12-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula's "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA's National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA's SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT's experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula. This presentation will provide an overview of these activities from a scientific and cloud computing applications perspective, identifying the strengths and weaknesses for deploying each project within an IaaS environment, and ways to collaborate with the Nebula or other cloud-user communities to collaborate on projects as they go forward.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Innovative approaches helpful to enhance knowledge on weather-related stroke events over a wide geographical area and a large population.

PubMed

Morabito, Marco; Crisci, Alfonso; Vallorani, Roberto; Modesti, Pietro Amedeo; Gensini, Gian Franco; Orlandini, Simone

2011-03-01

Results on the effect of weather on stroke occurrences are still confusing and controversial. The aim of this study was to retrospectively investigate in Tuscany (central Italy) the weather-related stroke events through the use of an innovative source of weather data (Reanalysis) together with an original statistical approach to quantify the prompt/delayed health effects of both cold and heat exposures. Daily stroke hospitalizations and meteorologic data from the Reanalysis 2 Achieve were obtained for the period 1997 to 2007. Generalized linear and additive models and an innovative modeling approach, the constrained segmented distributed lag model, were applied. Both daily averages and day-to-day changes of air temperature and geopotential height (a measure that approximates the mean surface pressure) were selected as independent predictors of all stroke occurrences. In particular, a 5°C temperature decrease was associated with 16.5% increase of primary intracerebral hemorrhage of people ≥65 years of age. A general short-term cold effect on hospitalizations limited to 1 week after exposure was observed and, for the first time, a clear harvesting effect (deficit of hospitalization) for cold-related primary intracerebral hemorrhage was described. Day-to-day changes of meteorologic parameters disclosed characteristic U- and J-shaped relationships with stroke occurrences. Thanks to the intrinsic characteristic of Reanalysis, these results might simply be implemented in an operative forecast system regarding weather-related stroke events with the aim to develop preventive health plans.

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

NASA Astrophysics Data System (ADS)

Dhanya, M.; Chandrasekar, A.

2016-02-01

The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.

Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Atmospheric signature of the Agulhas current

NASA Astrophysics Data System (ADS)

Stela Nkwinkwa Njouodo, Arielle; Koseki, Shunya; Rouault, Mathieu; Keenlyside, Noel

2017-04-01

Satellite observation and Climate Forecast System Reanalysis (CFSR) are used to map the influence of the Agulhas current on local annual precipitation in Southern Africa. The pressure adjustment mechanism is applied over the Agulhas current region. Results unfold that the narrow band of precipitation above the Agulhas Current is collocated with surface wind convergence, sea surface temperature (SST) Laplacian and sea level pressure (SLP) Laplacian. Relationship between SLP Laplacian and wind convergence is found, with 0.54 correlation coefficient statistically significant. In the free troposphere, the band of precipitation above the Agulhas current is collocated with the wind divergence and the upward motion of wind velocity. The warm waters from the Agulhas current can influence local precipitation.

Feasibility of performing high resolution cloud-resolving simulations of historic extreme events: The San Fruttuoso (Liguria, italy) case of 1915.

NASA Astrophysics Data System (ADS)

Parodi, Antonio; Boni, Giorgio; Ferraris, Luca; Gallus, William; Maugeri, Maurizio; Molini, Luca; Siccardi, Franco

2017-04-01

Recent studies show that highly localized and persistent back-building mesoscale convective systems represent one of the most dangerous flash-flood producing storms in the north-western Mediterranean area. Substantial warming of the Mediterranean Sea in recent decades raises concerns over possible increases in frequency or intensity of these types of events as increased atmospheric temperatures generally support increases in water vapor content. Analyses of available historical records do not provide a univocal answer, since these may be likely affected by a lack of detailed observations for older events. In the present study, 20th Century Reanalysis Project initial and boundary condition data in ensemble mode are used to address the feasibility of performing cloud-resolving simulations with 1 km horizontal grid spacing of a historic extreme event that occurred over Liguria (Italy): The San Fruttuoso case of 1915. The proposed approach focuses on the ensemble Weather Research and Forecasting (WRF) model runs, as they are the ones most likely to best simulate the event. It is found that these WRF runs generally do show wind and precipitation fields that are consistent with the occurrence of highly localized and persistent back-building mesoscale convective systems, although precipitation peak amounts are underestimated. Systematic small north-westward position errors with regard to the heaviest rain and strongest convergence areas imply that the Reanalysis members may not be adequately representing the amount of cool air over the Po Plain outflowing into the Liguria Sea through the Apennines gap. Regarding the role of historical data sources, this study shows that in addition to Reanalysis products, unconventional data, such as historical meteorological bulletins, newspapers and even photographs can be very valuable sources of knowledge in the reconstruction of past extreme events.

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Geomagnetic storm forecasting service StormFocus: 5 years online

NASA Astrophysics Data System (ADS)

Podladchikova, Tatiana; Petrukovich, Anatoly; Yermolaev, Yuri

2018-04-01

Forecasting geomagnetic storms is highly important for many space weather applications. In this study, we review performance of the geomagnetic storm forecasting service StormFocus during 2011-2016. The service was implemented in 2011 at SpaceWeather.Ru and predicts the expected strength of geomagnetic storms as measured by Dst index several hours ahead. The forecast is based on L1 solar wind and IMF measurements and is updated every hour. The solar maximum of cycle 24 is weak, so most of the statistics are on rather moderate storms. We verify quality of selection criteria, as well as reliability of real-time input data in comparison with the final values, available in archives. In real-time operation 87% of storms were correctly predicted while the reanalysis running on final OMNI data predicts successfully 97% of storms. Thus the main reasons for prediction errors are discrepancies between real-time and final data (Dst, solar wind and IMF) due to processing errors, specifics of datasets.

A Study of Subseasonal Predictability of the Atmospheric Circulation Low-frequency Modes based on SL-AV forecasts

NASA Astrophysics Data System (ADS)

Kruglova, Ekaterina; Kulikova, Irina; Khan, Valentina; Tischenko, Vladimir

2017-04-01

The subseasonal predictability of low-frequency modes and the atmospheric circulation regimes is investigated based on the using of outputs from global Semi-Lagrangian (SL-AV) model of the Hydrometcentre of Russia and Institute of Numerical Mathematics of Russian Academy of Science. Teleconnection indices (AO, WA, EA, NAO, EU, WP, PNA) are used as the quantitative characteristics of low-frequency variability to identify zonal and meridional flow regimes with focus on control distribution of high impact weather patterns in the Northern Eurasia. The predictability of weekly and monthly averaged indices is estimated by the methods of diagnostic verification of forecast and reanalysis data covering the hindcast period, and also with the use of the recommended WMO quantitative criteria. Characteristics of the low frequency variability have been discussed. Particularly, it is revealed that the meridional flow regimes are reproduced by SL-AV for summer season better comparing to winter period. It is shown that the model's deterministic forecast (ensemble mean) skill at week 1 (days 1-7) is noticeably better than that of climatic forecasts. The decrease of skill scores at week 2 (days 8-14) and week 3( days 15-21) is explained by deficiencies in the modeling system and inaccurate initial conditions. It was noticed the slightly improvement of the skill of model at week 4 (days 22-28), when the condition of atmosphere is more determined by the flow of energy from the outside. The reliability of forecasts of monthly (days 1-30) averaged indices is comparable to that at week 1 (days 1-7). Numerical experiments demonstrated that the forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the using of probabilistic approach based on ensemble forecasts. It is shown that the quality of forecasts of the regimes of circulation like blocking is higher, than that of zonal flow.

Performance and quality assessment of the global ocean eddy-permitting physical reanalysis GLORYS2V4.

NASA Astrophysics Data System (ADS)

Garric, Gilles; Parent, Laurent; Greiner, Eric; Drévillon, Marie; Hamon, Mathieu; Lellouche, Jean-Michel; Régnier, Charly; Desportes, Charles; Le Galloudec, Olivier; Bricaud, Clement; Drillet, Yann; Hernandez, Fabrice; Le Traon, Pierre-Yves

2017-04-01

The purpose of this presentation is to give an overview of the recent upgrade of GLORYS2 (version 4 and GLORYS2V4 hereafter), the latest ocean reanalysis produced at Mercator Ocean that covers the altimetry era (1993-2015) in the framework of Copernicus Marine Environment Monitoring Service (CMEMS; http://marine.copernicus.eu/). The reanalysis is run at eddy-permitting resolution (¼° horizontal resolution and 75 vertical levels) with the NEMO model and driven at the surface by ERA-Interim reanalysis from ECMWF (European Centre for Medium-Range Weather Forecasts). The reanalysis system uses a multi-data and multivariate reduced order Kalman filter based on the singular extended evolutive Kalman (SEEK) filter formulation together with a 3D-VAR large scale bias correction. The assimilated observations are along-track satellite altimetry, sea surface temperature, sea ice concentration and in-situ profiles of temperature and salinity. With respect to the previous version (GLORYS2V3), GLORYS2V4 contains a number of improvements. In particular: a) new initial temperature and salinity conditions derived from EN4 data base with a better mass equilibrium with altimetry, b) the use of the updated delayed mode CORA in situ observations from CMEMS, c) a new hybrid Mean Dynamical Topography (MDT) for the assimilation scheme referenced over the 1993-2013 period, d) a better observation operator for altimetry observations for the data assimilation scheme: e) A correction of large scale ERA-Interim atmospheric surface (precipitations and radiative) fluxes as in GLORYS2V3 but towards new satellite data set f) an update of the climatological runoff data base by using the latest version of Dai's 2009 data set for the global ocean together with better account of freshwater fluxes from polar ice sheet's glaciers. The presentation will show that the new reanalysis outperforms the previous version in many aspects such as biases and root mean squared error and, especially in representing the variability of global heat and salt content and associated steric sea level in the last two decades. The dataset is available in NetCDF format and GLORYS2V4 best analysis products are distributed onto the CMEMS data portal.

Evaluation of energy fluxes in the NCEP climate forecast system version 2.0 (CFSv2)

NASA Astrophysics Data System (ADS)

Rai, Archana; Saha, Subodh Kumar

2018-01-01

The energy fluxes at the surface and top of the atmosphere (TOA) from a long free run by the NCEP climate forecast system version 2.0 (CFSv2) are validated against several observation and reanalysis datasets. This study focuses on the annual mean energy fluxes and tries to link it with the systematic cold biases in the 2 m air temperature, particularly over the land regions. The imbalance in the long term mean global averaged energy fluxes are also evaluated. The global averaged imbalance at the surface and at the TOA is found to be 0.37 and 6.43 Wm-2, respectively. It is shown that CFSv2 overestimates the land surface albedo, particularly over the snow region, which in turn contributes to the cold biases in 2 m air temperature. On the other hand, surface albedo is highly underestimated over the coastal region around Antarctica and that may have contributed to the warm bias over that oceanic region. This study highlights the need for improvements in the parameterization of snow/sea-ice albedo scheme for a realistic simulation of surface temperature and that may have implications on the global energy imbalance in the model.

Precipitation Seasonality over the Indian Subcontinent: Assessment of Gauge, Reanalyses and Satellite Products

NASA Astrophysics Data System (ADS)

Renwick, J. A.; Rana, S.; McGregor, J.

2015-12-01

This work addresses the seasonal (winter, pre-monsoon, monsoon and post-monsoon) performance of seven precipitation products from three different data sources: gridded station data, satellite-derived data and reanalyses products over the Indian Subcontinent, for a period of 10 years (1997/98 to 2006/07). Precipitation products evaluated are the Asian Precipitation - Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Prediction Center unified gauge (CPC-uni), the Global Precipitation Climatology project (GPCP), Tropical Rainfall Measuring Mission (TRMM) post real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6 and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions were drawn for the post-monsoon season, with the exception of 3B42-V7, which underestimates post-monsoon precipitation. Over mountainous regions 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which is likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals.

Simulation of a Severe Autumn/Winter Drought in Eastern China by Regional Atmospheric Modeling System(RAMS)

NASA Astrophysics Data System (ADS)

Meng, Chunchun; Ma, Yaoming

2016-04-01

Compared with European Centre for Medium-Range Weather Forecasts (ERA-interim) Reanalysis data and Global Summary Of Day (GSOD) observation data, the outcomes from RAMS of the 2008/2009 severe autumn/winter drought in eastern china are analyzed in this study. The reanalysis data showed that most parts of north China are controlled by northwest wind which was accompanied by cold air, the warm and moist air from South Sea is so weak to meet with cold air, therefore forming a circulation which is unfavorable for the formation of precipitation over Eastern China. RAMS performs very well over the simulation of this atmospheric circulation, so do the rainfall and air temperature over China and where the drought occurred. Meanwhile, the simulation of the time series of precipitation and temperature behaves excellent, the square of correlation coefficient between simulations and observations reached above 0.8. Although the performance of RAMS on this drought simulation is fairly accurate, there is amount of research work to be continued to complete a more realistic simulation. KEY WORDS RAMS; severe drought; numerical simulation; atmospheric circulation; precipitation and air temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zarzycki, Colin M.; Thatcher, Diana R.; Jablonowski, Christiane

This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high-resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable-resolution Community Atmosphere Model (CAM) at two different resolutions (DX 55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, DX 38 km), and the ERA-Interim Reanalysis (ERA-I, DX 80 km).more » The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA-I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold-core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase.« less

Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir

PubMed Central

Yang, Yan; Wang, Guoqiang; Wang, Lijing; Yu, Jingshan; Xu, Zongxue

2014-01-01

Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction - Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend - surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography. PMID:25409467

A similarity retrieval approach for weighted track and ambient field of tropical cyclones

NASA Astrophysics Data System (ADS)

Li, Ying; Xu, Luan; Hu, Bo; Li, Yuejun

2018-03-01

Retrieving historical tropical cyclones (TC) which have similar position and hazard intensity to the objective TC is an important means in TC track forecast and TC disaster assessment. A new similarity retrieval scheme is put forward based on historical TC track data and ambient field data, including ERA-Interim reanalysis and GFS and EC-fine forecast. It takes account of both TC track similarity and ambient field similarity, and optimal weight combination is explored subsequently. Result shows that both the distance and direction errors of TC track forecast at 24-hour timescale follow an approximately U-shape distribution. They tend to be large when the weight assigned to track similarity is close to 0 or 1.0, while relatively small when track similarity weight is from 0.2˜0.7 for distance error and 0.3˜0.6 for direction error.

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

NASA Technical Reports Server (NTRS)

da Silva, Arlindo; Redder, Christopher

2010-01-01

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

Climate Reanalysis: Progress and Future Prospects

NASA Technical Reports Server (NTRS)

Gelaro, Ron

2018-01-01

Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of observations, typically spanning an extended segment of the historical data record. The process relies on an underlying model to combine often-disparate observations in a physically consistent manner, enabling production of gridded data sets for a broad range of applications including the study of historical weather events, preparation of climatologies, business sector development and, more recently, climate monitoring. Over the last few decades, several generations of reanalyses of the global atmosphere have been produced by various operational and research centers, focusing more or less on the period of regular conventional and satellite observations beginning in the mid to late twentieth century. There have also been successful efforts to extend atmospheric reanalyses back to the late nineteenth and early twentieth centuries, using mostly surface observations. Much progress has resulted from (and contributed to) advancements in numerical weather prediction, especially improved models and data assimilation techniques, increased computing capacity, the availability of new observation types and efforts to recover and improve the quality of historical ones. The recent extension of forecast systems that allow integrated modeling of meteorological, oceanic, land surface, and chemical variables provide the basic elements for coupled data assimilation. This has opened the door to the development of a new generation of coupled reanalyses of the Earth system, or integrated Earth system analyses (IESA). Evidence so far suggests that this approach can improve the analysis of currently uncoupled components of the Earth system, especially at their interface, and lead to increased predictability. However, extensive analysis coupling as envisioned for IESA, while progressing, still presents significant challenges. These include model biases that can be exacerbated when coupled, component systems with different physical characteristics and different spatial and temporal scales, and component observations in different media with different spatial and temporal frequencies and different latencies. Quantification of uncertainty in reanalyses is also a critical challenge and is important for expanding their utility as a tool for climate change assessment. This talk provides a brief overview of the progress of reanalysis development during recent decades, and describes remaining challenges in the progression toward coupled Earth system reanalyses.

Using MERRA Gridded Innovation for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

NASA Astrophysics Data System (ADS)

da Silva, A.; Redder, C. R.

2010-12-01

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The Project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

The eSurge-Venice project: altimeter and scatterometer satellite data to improve the storm surge forecasting in the city of Venice

NASA Astrophysics Data System (ADS)

Zecchetto, Stefano; De Biasio, Francesco; Umgiesser, Georg; Bajo, Marco; Vignudelli, Stefano; Papa, Alvise; Donlon, Craig; Bellafiore, Debora

2013-04-01

On the framework of the Data User Element (DUE) program, the European Space Agency is funding a project to use altimeter Total Water Level Envelope (TWLE) and scatterometer wind data to improve the storm surge forecasting in the Adriatic Sea and in the city of Venice. The project will: a) Select a number of Storm Surge Events occurred in the Venice lagoon in the period 1999-present day b) Provide the available satellite Earth Observation (EO) data related to the Storm Surge Events, mainly satellite winds and altimeter data, as well as all the available in-situ data and model forecasts c) Provide a demonstration Near Real Time service of EO data products and services in support of operational and experimental forecasting and warning services d) Run a number of re-analysis cases, both for historical and contemporary storm surge events, to demonstrate the usefulness of EO data The re-analysis experiments, based on hindcasts performed by the finite element 2-D oceanographic model SHYFEM (https://sites.google.com/site/shyfem/), will 1. use different forcing wind fields (calibrated and not calibrated with satellite wind data) 2. use Storm Surge Model initial conditions determined from altimeter TWLE data. The experience gained working with scatterometer and Numerical Weather Prediction (NWP) winds in the Adriatic Sea tells us that the bias NWP-Scatt wind is negative and spatially and temporally not uniform. In particular, a well established point is that the bias is higher close to coasts then offshore. Therefore, NWP wind speed calibration will be carried out on each single grid point in the Adriatic Sea domain over the period of a Storm Surge Event, taking into account of existing published methods. Point #2 considers two different methodologies to be used in re-analysis tests. One is based on the use of the TWLE values from altimeter data in the Storm Surge Model (SSM), applying data assimilation methodologies and trying to optimize the initial conditions of the simulation.The second possibility is an indirect exploitation of the TWLE data from altimeter in an ensemble-like framework, obtained by slight variations of the external forcing. In this case the wind data from NWP models will be weakly altered (shifted in phase), the drag coefficient will be modified, and the initial condition of the model slightly shifted in time to account for the uncertainty of these factors. This contribution will illustrate the geophysical context of work and outline the results.

Assessment of moisture budget over West Africa using MERRA-2's aerological model and satellite data

NASA Astrophysics Data System (ADS)

Igbawua, Tertsea; Zhang, Jiahua; Yao, Fengmei; Zhang, Da

2018-02-01

The study assessed the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and MERRA-2 aerological (P-E*) model in reproducing the salient features of West Africa water balance including its components from 1980 to 2013. In this study we have shown that recent reanalysis efforts have generated imbalances between regional integrated precipitation (P) and surface evaporation (E), and the effect is more in the newly released MERRA-2. The atmospheric water balance of MERRA and MERRA-2 were inter-compared and thereafter compared with model forecast output of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-I) and Japanese 55-year Reanalysis (JRA-55). Results indicated that a bias of 12-20 (5-13) mm/month in MERRA-2 (ERA-I) leads to the classification of the Sahel (14°N-20°N) as a moisture source during the West African Summer Monsoon. Comparisons between MERRA/MERRA-2 and prognostic fields from two ERA-I and JRA-55 indicated that the average P-E* in MERRA is 18.94 (52.24) mm/month which is less than ERA-I (JRA-55) over Guinea domain and 25.03 (4.53) mm/month greater than ERA-I (JRA-55) over the Sahel. In MERRA-2, average P-E* indicated 25.76 (59.06) mm/month which is less than ERA-I (JRA-55) over Guinea and 73.72 (94.22) mm/month less than ERA-I (JRA-55) over the Sahel respectively. These imbalances are due to adjustments in data assimilation methods, satellite calibration and observational data base. The change in convective P parameterization and increased re-evaporation of P in MERRA-2 is suggestive of the cause of positive biases in P and E. The little disagreements between MERRA/MERRA-2 and CRU precipitation highlights one of the major challenges associated with climate research in West Africa and major improvements in observation data and surface fluxes from reanalysis remain vital.

A preliminary experiment for the long-term regional reanalysis over Japan assimilating conventional observations with NHM-LETKF

NASA Astrophysics Data System (ADS)

Fukui, Shin; Iwasaki, Toshiki; Saito, Kazuo; Seko, Hiromu; Kunii, Masaru

2016-04-01

Several long-term global reanalyses have been produced by major operational centres and have contributed to the advance of weather and climate researches considerably. Although the horizontal resolutions of these global reanalyses are getting higher partly due to the development of computing technology, they are still too coarse to reproduce local circulations and precipitation realistically. To solve this problem, dynamical downscaling is often employed. However, the forcing from lateral boundaries only cannot necessarily control the inner fields especially in long-term dynamical downscaling. Regional reanalysis is expected to overcome the difficulty. To maintain the long-term consistency of the analysis quality, it is better to assimilate only the conventional observations that are available in long period. To confirm the effectiveness of the regional reanalysis, some assimilation experiments are performed. In the experiments, only conventional observations (SYNOP, SHIP, BUOY, TEMP, PILOT, TC-Bogus) are assimilated with the NHM-LETKF system, which consists of the nonhydrostatic model (NHM) of the Japan Meteorological Agency (JMA) and the local ensemble transform Kalman filter (LETKF). The horizontal resolution is 25 km and the domain covers Japan and its surroundings. Japanese 55-year reanalysis (JRA-55) is adopted as the initial and lateral boundary conditions for the NHM-LETKF forecast-analysis cycles. The ensemble size is 10. The experimental period is August 2014 as a representative of warm season for the region. The results are verified against the JMA's operational Meso-scale Analysis, which is produced with assimilating observation data including various remote sensing observations using a 4D-Var scheme, and compared with those of the simple dynamical downscaling experiment without data assimilation. Effects of implementation of lateral boundary perturbations derived from an EOF analysis of JRA-55 over the targeted domain are also examined. The comparison proposes that the assimilation system can reproduce more accurate fields than dynamical downscaling. The implementation of the lateral boundary perturbations implies that the perturbations contribute to providing more appropriate ensemble spreads, though the perturbations are not necessarily consistent to those of the inner fields given by NHM-LETKF.

Forecasting Future Sea Ice Conditions in the MIZ: A Lagrangian Approach

DTIC Science & Technology

2013-09-30

www.mcgill.ca/meteo/people/tremblay LONG-TERM GOALS 1- Determine the source regions for sea ice in the seasonally ice-covered zones (SIZs...distribution of sea ice cover and transport pathways. 2- Improve our understanding of the strengths and/or limitations of GCM predictions of future...ocean currents, RGPS sea ice deformation, Reanalysis surface wind , surface radiative fluxes, etc. Processing the large datasets involved is a tedious

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies and its application to four recent severe regional drought events in China

NASA Astrophysics Data System (ADS)

Liu, Z.; LU, G.; He, H.; Wu, Z.; He, J.

2017-12-01

Reliable drought prediction is fundamental for seasonal water management. Considering that drought development is closely related to the spatio-temporal evolution of large-scale circulation patterns, we develop a conceptual prediction model of seasonal drought processes based on atmospheric/oceanic Standardized Anomalies (SA). It is essentially the synchronous stepwise regression relationship between 90-day-accumulated atmospheric/oceanic SA-based predictors and 3-month SPI updated daily (SPI3). It is forced with forecasted atmospheric and oceanic variables retrieved from seasonal climate forecast systems, and it can make seamless drought prediction for operational use after a year-to-year calibration. Simulation and prediction of four severe seasonal regional drought processes in China were forced with the NCEP/NCAR reanalysis datasets and the NCEP Climate Forecast System Version 2 (CFSv2) operationally forecasted datasets, respectively. With the help of real-time correction for operational application, model application during four recent severe regional drought events in China revealed that the model is good at development prediction but weak in severity prediction. In addition to weakness in prediction of drought peak, the prediction of drought relief is possible to be predicted as drought recession. This weak performance may be associated with precipitation-causing weather patterns during drought relief. Based on initial virtual analysis on predicted 90-day prospective SPI3 curves, it shows that the 2009/2010 drought in Southwest China and 2014 drought in North China can be predicted and simulated well even for the prospective 1-75 day. In comparison, the prospective 1-45 day may be a feasible and acceptable lead time for simulation and prediction of the 2011 droughts in Southwest China and East China, after which the simulated and predicted developments clearly change.

Assessment of upper tropospheric and stratospheric water vapor and ozone in reanalyses as part of S-RIP

NASA Astrophysics Data System (ADS)

Davis, Sean M.; Hegglin, Michaela I.; Fujiwara, Masatomo; Dragani, Rossana; Harada, Yayoi; Kobayashi, Chiaki; Long, Craig; Manney, Gloria L.; Nash, Eric R.; Potter, Gerald L.; Tegtmeier, Susann; Wang, Tao; Wargan, Krzysztof; Wright, Jonathon S.

2017-10-01

Reanalysis data sets are widely used to understand atmospheric processes and past variability, and are often used to stand in as "observations" for comparisons with climate model output. Because of the central role of water vapor (WV) and ozone (O3) in climate change, it is important to understand how accurately and consistently these species are represented in existing global reanalyses. In this paper, we present the results of WV and O3 intercomparisons that have been performed as part of the SPARC (Stratosphere-troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The comparisons cover a range of timescales and evaluate both inter-reanalysis and observation-reanalysis differences. We also provide a systematic documentation of the treatment of WV and O3 in current reanalyses to aid future research and guide the interpretation of differences amongst reanalysis fields.The assimilation of total column ozone (TCO) observations in newer reanalyses results in realistic representations of TCO in reanalyses except when data coverage is lacking, such as during polar night. The vertical distribution of ozone is also relatively well represented in the stratosphere in reanalyses, particularly given the relatively weak constraints on ozone vertical structure provided by most assimilated observations and the simplistic representations of ozone photochemical processes in most of the reanalysis forecast models. However, significant biases in the vertical distribution of ozone are found in the upper troposphere and lower stratosphere in all reanalyses.In contrast to O3, reanalysis estimates of stratospheric WV are not directly constrained by assimilated data. Observations of atmospheric humidity are typically used only in the troposphere, below a specified vertical level at or near the tropopause. The fidelity of reanalysis stratospheric WV products is therefore mainly dependent on the reanalyses' representation of the physical drivers that influence stratospheric WV, such as temperatures in the tropical tropopause layer, methane oxidation, and the stratospheric overturning circulation. The lack of assimilated observations and known deficiencies in the representation of stratospheric transport in reanalyses result in much poorer agreement amongst observational and reanalysis estimates of stratospheric WV. Hence, stratospheric WV products from the current generation of reanalyses should generally not be used in scientific studies.

Ensemble cloud-resolving modelling of a historic back-building mesoscale convective system over Liguria: the San Fruttuoso case of 1915

NASA Astrophysics Data System (ADS)

Parodi, Antonio; Ferraris, Luca; Gallus, William; Maugeri, Maurizio; Molini, Luca; Siccardi, Franco; Boni, Giorgio

2017-05-01

Highly localized and persistent back-building mesoscale convective systems represent one of the most dangerous flash-flood-producing storms in the north-western Mediterranean area. Substantial warming of the Mediterranean Sea in recent decades raises concerns over possible increases in frequency or intensity of these types of events as increased atmospheric temperatures generally support increases in water vapour content. However, analyses of the historical record do not provide a univocal answer, but these are likely affected by a lack of detailed observations for older events. In the present study, 20th Century Reanalysis Project initial and boundary condition data in ensemble mode are used to address the feasibility of performing cloud-resolving simulations with 1 km horizontal grid spacing of a historic extreme event that occurred over Liguria: the San Fruttuoso case of 1915. The proposed approach focuses on the ensemble Weather Research and Forecasting (WRF) model runs that show strong convergence over the Ligurian Sea (17 out of 56 members) as these runs are the ones most likely to best simulate the event. It is found that these WRF runs generally do show wind and precipitation fields that are consistent with the occurrence of highly localized and persistent back-building mesoscale convective systems, although precipitation peak amounts are underestimated. Systematic small north-westward position errors with regard to the heaviest rain and strongest convergence areas imply that the reanalysis members may not be adequately representing the amount of cool air over the Po Plain outflowing into the Ligurian Sea through the Apennines gap. Regarding the role of historical data sources, this study shows that in addition to reanalysis products, unconventional data, such as historical meteorological bulletins, newspapers, and even photographs, can be very valuable sources of knowledge in the reconstruction of past extreme events.

Three-dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

NASA Astrophysics Data System (ADS)

Tang, Shuaiqi; Zhang, Minghua

2015-08-01

Atmospheric vertical velocities and advective tendencies are essential large-scale forcing data to drive single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulations (LESs). However, they cannot be directly measured from field measurements or easily calculated with great accuracy. In the Atmospheric Radiation Measurement Program (ARM), a constrained variational algorithm (1-D constrained variational analysis (1DCVA)) has been used to derive large-scale forcing data over a sounding network domain with the aid of flux measurements at the surface and top of the atmosphere (TOA). The 1DCVA algorithm is now extended into three dimensions (3DCVA) along with other improvements to calculate gridded large-scale forcing data, diabatic heating sources (Q1), and moisture sinks (Q2). Results are presented for a midlatitude cyclone case study on 3 March 2000 at the ARM Southern Great Plains site. These results are used to evaluate the diabatic heating fields in the available products such as Rapid Update Cycle, ERA-Interim, National Centers for Environmental Prediction Climate Forecast System Reanalysis, Modern-Era Retrospective Analysis for Research and Applications, Japanese 55-year Reanalysis, and North American Regional Reanalysis. We show that although the analysis/reanalysis generally captures the atmospheric state of the cyclone, their biases in the derivative terms (Q1 and Q2) at regional scale of a few hundred kilometers are large and all analyses/reanalyses tend to underestimate the subgrid-scale upward transport of moist static energy in the lower troposphere. The 3DCVA-gridded large-scale forcing data are physically consistent with the spatial distribution of surface and TOA measurements of radiation, precipitation, latent and sensible heat fluxes, and clouds that are better suited to force SCMs, CRMs, and LESs. Possible applications of the 3DCVA are discussed.

Surface Water and Energy Budgets for Sub-Saharan Africa in GFDL Coupled Climate Model

NASA Astrophysics Data System (ADS)

Tian, D.; Wood, E. F.; Vecchi, G. A.; Jia, L.; Pan, M.

2015-12-01

This study compare surface water and energy budget variables from the Geophysical Fluid Dynamics Laboratory (GFDL) FLOR models with the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Princeton University Global Meteorological Forcing Dataset (PGF), and PGF-driven Variable Infiltration Capacity (VIC) model outputs, as well as available observations over the sub-Saharan Africa. The comparison was made for four configurations of the FLOR models that included FLOR phase 1 (FLOR-p1) and phase 2 (FLOR-p2) and two phases of flux adjusted versions (FLOR-FA-p1 and FLOR-FA-p2). Compared to p1, simulated atmospheric states in p2 were nudged to the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The seasonal cycle and annual mean of major surface water (precipitation, evapotranspiration, runoff, and change of storage) and energy variables (sensible heat, ground heat, latent heat, net solar radiation, net longwave radiation, and skin temperature) over a 34-yr period during 1981-2014 were compared in different regions in sub-Saharan Africa (West Africa, East Africa, and Southern Africa). In addition to evaluating the means in three sub-regions, empirical orthogonal functions (EOFs) analyses were conducted to compare both spatial and temporal characteristics of water and energy budget variables from four versions of GFDL FLOR, NCEP CFSR, PGF, and VIC outputs. This presentation will show how well each coupled climate model represented land surface physics and reproduced spatiotemporal characteristics of surface water and energy budget variables. We discuss what caused differences in surface water and energy budgets in land surface components of coupled climate model, climate reanalysis, and reanalysis driven land surface model. The comparisons will reveal whether flux adjustment and nudging would improve depiction of the surface water and energy budgets in coupled climate models.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

2015-04-01

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue Downscaling method for Hydrology) statistical downscaling method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an ensemble of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are considered to correct monthly precipitation and temperature time series. The first one applies two new analogy steps, using the sea surface temperature (SST) and the large-scale two-meter temperature. The second method is a calendar selection that keeps the closest analogue dates in the year for each target date. A sensitivity study has been performed to assess the final number of analogues dates to retain for each method. A comparison to Safran over 1958-2010 shows that biases on the interannual cycle of precipitation and temperature are strongly reduced with both methods. Using two supplementary analogy levels moreover leads to a large improvement of correlation in seasonal temperature time series. These two methods have also been validated before 1958 thanks to both raw observations and homogenized time series. The two post-processing methods come with some advantages and drawbacks. The calendar selection allows to slightly better correct for seasonal biases in precipitation and is therefore adapted in a forecasting context. The selection with two supplementary analogy levels would allow for possible season shifts and SST trends and is therefore better suited for climate reconstruction and climate change studies. Compo, G. P. et al. (2011). The Twentieth Century Reanalysis Project. Quarterly Journal of the Royal Meteorological Society, 137:1-28. doi: 10.1002/qj.776 Radanovics, S., Vidal, J.-P., Sauquet, E., Ben Daoud, A., and Bontron, G. (2013). Optimising predictor domains for spatially coherent precipitation downscaling. Hydrology and Earth System Sciences, 17:4189-4208. doi:10.5194/hess-17-4189-2013 Vidal, J.-P ., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010). A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi:10.1002/joc.2003

The CMEMS IBI-MFC Forecasting Service in 2017: Evolution and Novelties associated to the CMEMS service release

NASA Astrophysics Data System (ADS)

Lorente, Pablo; Sotillo, Marcos G.; Gutknecht, Elodie; Dabrowski, Tomasz; Aouf, Lotfi; Toledano, Cristina; Amo-Baladron, Arancha; Aznar, Roland; De Pascual, Alvaro; Levier, Bruno; Bowyer, Peter; Rainaud, Romain; Alvarez-Fanjul, Enrique

2017-04-01

The IBI-MFC (Iberia-Biscay-Ireland Monitoring & Forecasting Centre) has been providing daily ocean model estimates and forecasts of diverse physical parameters for the IBI regional seas since 2011, first in the frame of MyOcean projects and later as part of the Copernicus Marine Environment Monitoring Service (CMEMS). By April 2017, coincident with the V3 CMEMS Service Release, the IBI-MFC will extend their near real time (NRT) forecast capabilities. Two new operational IBI forecast systems will be operationally run to generate high resolution biochemical (BIO) and wave (WAV) products on the IBI area. The IBI-NRT-BIO forecast system, based on a 1/36° NEMO-PISCES model application, is run once a week coupled with the IBI physical forecast solution and nested to the CMEMS GLOBAL-BIO solution. On the other hand, the IBI-NRT-WAV system, based on a MeteoFrance-WAM 10km resolution model application, runs twice a day using ECMWF wind forcing. Among other novelties related to the evolution of the IBI physical (PHY) solution, it is worthwhile mentioning the provision, as part of the IBI-NRT-PHY product daily updated, of three-dimensional hourly data on specific areas within the IBI domain. The delivery of these new hourly data along the whole water column has been achieved after the request from IBI users, in order to foster downscaling approaches by providing coherent open boundary conditions to any potential high-resolution coastal model nested to IBI regional solution. An extensive skill assessment of IBI-NRT forecast products has been conducted through the NARVAL (North Atlantic Regional VALidation) web tool, by means of the automatic computation of statistical metrics and quality indicators. By now, this tool has been focused on the validation of the IBI-NRT-PHY system. Nowadays, NARVAL is facing a significant upgrade to validate the aforementioned new biogeochemical and wave IBI products. To this aim, satellite derived observations of chlorophyll and significant wave height will be used, together with in-situ wave parameters measured by mooring buoys. Within this validation framework, special emphasis has been placed on the intercomparison of different forecast model solutions in overlapping areas in order to evaluate models' performances and prognostic capabilities. This common uncertainty estimates of IBI and other model solution is currently performed by NARVAL using both CMEMS forecast model sources (i.e. GLOBAL-MFC, MED-MFC and NWS-MFC) and non-CMEMS operational forecast solutions (mostly downstream application nested to the IBI solution). With respect to the IBI multi-year (MY) products, it is worth mentioning that the actual biogeochemical and physical reanalysis products will be re-run along year 2017, extending its time coverage backwards until 1992. Based on these IBI-MY products, a variety of climatic indicators related to essential oceanographic processes (i.e. western coastal upwelling or the Mediterranean Outflow Water) are currently being computed.

Effects of Implementing Subgrid-Scale Cloud-Radiation Interactions in a Regional Climate Model

NASA Astrophysics Data System (ADS)

Herwehe, J. A.; Alapaty, K.; Otte, T.; Nolte, C. G.

2012-12-01

Interactions between atmospheric radiation, clouds, and aerosols are the most important processes that determine the climate and its variability. In regional scale models, when used at relatively coarse spatial resolutions (e.g., larger than 1 km), convective cumulus clouds need to be parameterized as subgrid-scale clouds. Like many groups, our regional climate modeling group at the EPA uses the Weather Research & Forecasting model (WRF) as a regional climate model (RCM). One of the findings from our RCM studies is that the summertime convective systems simulated by the WRF model are highly energetic, leading to excessive surface precipitation. We also found that the WRF model does not consider the interactions between convective clouds and radiation, thereby omitting an important process that drives the climate. Thus, the subgrid-scale cloudiness associated with convective clouds (from shallow cumuli to thunderstorms) does not exist and radiation passes through the atmosphere nearly unimpeded, potentially leading to overly energetic convection. This also has implications for air quality modeling systems that are dependent upon cloud properties from the WRF model, as the failure to account for subgrid-scale cloudiness can lead to problems such as the underrepresentation of aqueous chemistry processes within clouds and the overprediction of ozone from overactive photolysis. In an effort to advance the climate science of the cloud-aerosol-radiation (CAR) interactions in RCM systems, as a first step we have focused on linking the cumulus clouds with the radiation processes. To this end, our research group has implemented into WRF's Kain-Fritsch (KF) cumulus parameterization a cloudiness formulation that is widely used in global earth system models (e.g., CESM/CAM5). Estimated grid-scale cloudiness and associated condensate are adjusted to account for the subgrid clouds and then passed to WRF's Rapid Radiative Transfer Model - Global (RRTMG) radiation schemes to affect the shortwave and longwave radiative processes. To evaluate the effects of implementing the subgrid-scale cloud-radiation interactions on WRF regional climate simulations, a three-year study period (1988-1990) was simulated over the CONUS using two-way nested domains with 108 km and 36 km horizontal grid spacing, without and with the cumulus feedbacks to radiation, and without and with some form of four dimensional data assimilation (FDDA). Initial and lateral boundary conditions (as well as data for the FDDA, when enabled) were supplied from downscaled NCEP-NCAR Reanalysis II (R2) data sets. Evaluation of the simulation results will be presented comparing regional surface precipitation and temperature statistics with North American Regional Reanalysis (NARR) data and Climate Forecast System Reanalysis (CFSR) data, respectively, as well as comparison with available surface radiation (SURFRAD) and satellite (CERES) observations. This research supports improvements in the EPA's WRF-CMAQ modeling system, leading to better predictions of present and future air quality and climate interactions in order to protect human health and the environment.

How well can the observed Arctic sea ice summer retreat and winter advance be represented in the NCEP Climate Forecast System version 2?

NASA Astrophysics Data System (ADS)

Collow, Thomas W.; Wang, Wanqiu; Kumar, Arun; Zhang, Jinlun

2017-09-01

The capability of a numerical model to simulate the statistical characteristics of the summer sea ice date of retreat (DOR) and the winter date of advance (DOA) is investigated using sea ice concentration output from the Climate Forecast System Version 2 model (CFSv2). Two model configurations are tested, the operational setting (CFSv2CFSR) which uses initial data from the Climate Forecast System Reanalysis, and a modified version (CFSv2PIOMp) which ingests sea ice thickness initialization data from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) and includes physics modifications for a more realistic representation of heat fluxes at the sea ice top and bottom. First, a method to define DOR and DOA is presented. Then, DOR and DOA are determined from the model simulations and observational sea ice concentration from the National Aeronautics and Space Administration (NASA). Means, trends, and detrended standard deviations of DOR and DOA are compared, along with DOR/DOA rates in the Arctic Ocean. It is found that the statistics are generally similar between the model and observations, although some regional biases exist. In addition, regions of new ice retreat in recent years are represented well in CFSv2PIOMp over the Arctic Ocean, in terms of both spatial extent and timing. Overall, CFSv2PIOMp shows a reduction in error throughout the Arctic. Based on results, it is concluded that the model produces a reasonable representation of the climatology and variability statistics of DOR and DOA in most regions. This assessment serves as a prerequisite for future predictability experiments.

Challenges in predicting and simulating summer rainfall in the eastern China

NASA Astrophysics Data System (ADS)

Liang, Ping; Hu, Zeng-Zhen; Liu, Yunyun; Yuan, Xing; Li, Xiaofan; Jiang, Xingwen

2018-05-01

To demonstrate the challenge of summer rainfall prediction and simulation in the eastern China, in this work, we examine the skill of the state-of-the-art climate models, evaluate the impact of sea surface temperature (SST) on forecast skill and estimate the predictability by using perfect model approach. The challenge is further demonstrated by assessing the ability of various reanalyses in capturing the observed summer rainfall variability in the eastern China and by examining the biases in reanalyses and in a climate model. Summer rainfall forecasts (hindcasts) initiated in May from eight seasonal forecast systems have low forecast skill with linear correlation of - 0.3 to 0.5 with observations. The low forecast skill is consistent with the low perfect model score ( 0.1-0.3) of atmospheric model forced by observed SST, due to the fact that external forcing (SST) may play a secondary role in the summer rainfall variation in the eastern China. This is a common feature for the climate variation over the middle and high latitude lands, where the internal dynamical processes dominate the rainfall variation in the eastern China and lead to low predictability, and external forcing (such as SST) plays a secondary role and is associated with predictable fraction. Even the reanalysis rainfall has some remarkable disagreements with the observation. Statistically, more than 20% of the observed variance is not captured by the mean of six reanalyses. Among the reanalyses, JRA55 stands out as the most reliable one. In addition, the reanalyses and climate model have pronounced biases in simulating the mean rainfall. These defaults mean an additional challenge in predicting the summer rainfall variability in the eastern China that has low predictability in nature.

An assessment of tropical cyclone representation in a regional reanalysis and a shape metric methodology for studying the evolving precipitation structure prior to and during landfall

NASA Astrophysics Data System (ADS)

Zick, Stephanie E.

Tropical cyclone (TC) precipitation is intricately organized with multiple scales of phenomena collaborating to harness the massive energy required to support these storms. During landfall, a TC leaves the tropical oceanic environment and encounters a wide range of continental air mass regimes. Although evolving precipitation patterns are qualitatively observed in these storms during landfall, the timing and spatial variability of these structural changes have yet to be quantified or documented. This dissertation integrates meteorological and geographic concepts to explore the representation and evolution of TC rainfall at the crucial time of landfall when coastal and inland communities and environments are most vulnerable to TC-associated flooding. This research begins with a two-part assessment of TC representation in the North American Regional Reanalysis (NARR), which is selected for its documented skill in characterizing North American precipitation patterns. Due to the sparsely available data over the tropical oceans, spatial biases exist in both global and regional reanalysis datasets. However, within the NARR the introduction of over-ocean precipitation assimilation in 2004 leads to an improved analysis of TC warm core structure, which results in an improved precipitation forecast. Collectively, these studies highlight the need for sophisticated observational and data assimilation systems. Specifically, the development of new, novel precipitation assimilation techniques will be valuable to the construction of better-quality forecasting tools with more authentic TC representation. In the third study, the fundamental geographic concept of compactness is utilized to construct a shape metric methodology for investigating (a) the overall evolution of and (b) the spatiotemporal positions of significant changes to synoptic-scale precipitation structure. These metrics encompass the characteristic geometries of TCs moving into the mid-latitudes: asymmetry, fragmentation, and dispersiveness. In 2004-2012 TCs, increasing (decreasing) compactness is observed in the eastern and central (western) Gulf of Mexico. Dispersiveness increases prior to landfall in most cases; however, asymmetry and fragmentation increase more commonly in western (versus eastern) Gulf landfalls. These results indicate that structural changes occur in advance of landfall, while the TC inner core is positioned over warm Gulf of Mexico waters, particularly in storms that make landfall in the northern and western Gulf States.

Convection in Extratropical Cyclones: Analysis of GPM, NexRAD, GCMs and Re-Analysis

NASA Astrophysics Data System (ADS)

Jeyaratnam, J.; Booth, J. F.; Naud, C. M.; Luo, J.

2017-12-01

Extratropical Cyclones (ETCs) are the most common cause of extreme precipitation in mid-latitudes and are important in the general atmospheric circulation as they redistribute moisture and heat. Isentropic lifting, upright convection, and slantwise convection are mechanisms of vertical motion within an ETC, which deliver different rain rates and might respond differently to global warming. In this study we compare different metrics for identifying convection within the ETC's and calculate the relative contribution of convection to total ETC precipitation. We determine if convection occurs preferentially in specific regions of the storm and decide how to best utilize GPM retrievals covering other parts of the mid-latitudes. Additionally, mid-latitude cyclones are tracked and composites of these tracked cyclones are compared amongst multiple versions of Global Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) prototype models and re-analysis data; Model Diagnostic Task Force (MDTF) Geophysical Fluid Dynamics Laboratory (GFDL) using a two-plume convection scheme, MDTF GFDL using the Donner convection scheme, Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and European Reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF).

Characterisation of Special Sensor Microwave Water Vapor Profiler (SSM/T-2) radiances using radiative transfer simulations from global atmospheric reanalyses

NASA Astrophysics Data System (ADS)

Kobayashi, Shinya; Poli, Paul; John, Viju O.

2017-02-01

The near-global and all-sky coverage of satellite observations from microwave humidity sounders operating in the 183 GHz band complement radiosonde and aircraft observations and satellite infrared clear-sky observations. The Special Sensor Microwave Water Vapor Profiler (SSM/T-2) of the Defense Meteorological Satellite Program began operations late 1991. It has been followed by several other microwave humidity sounders, continuing today. However, expertise and accrued knowledge regarding the SSM/T-2 data record is limited because it has remained underused for climate applications and reanalyses. In this study, SSM/T-2 radiances are characterised using several global atmospheric reanalyses. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), the first ECMWF reanalysis of the 20th-century (ERA-20C), and the Japanese 55-year Reanalysis (JRA-55) are projected into SSM/T-2 radiance space using a fast radiative transfer model. The present study confirms earlier indications that the polarisation state of SSM/T-2 antenna is horizontal (not vertical) in the limit of nadir viewing. The study also formulates several recommendations to improve use of the SSM/T-2 measurement data in future fundamental climate data records or reanalyses. Recommendations are (1) to correct geolocation errors, especially for DMSP 14; (2) to blacklist poor quality data identified in the paper; (3) to correct for inter-satellite biases, estimated here on the order of 1 K, by applying an inter-satellite recalibration or, for reanalysis, an automated (e.g., variational) bias correction; and (4) to improve precipitating cloud filtering or, for reanalysis, consider an all-sky assimilation scheme where radiative transfer simulations account for the scattering effect of hydrometeors.

An Assessment of the Subseasonal Predictability of Severe Thunderstorm Environments and Activity using the Climate Forecast System Version 2

NASA Astrophysics Data System (ADS)

Stepanek, Adam J.

The prospect for skillful long-term predictions of atmospheric conditions known to directly contribute to the onset and maintenance of severe convective storms remains unclear. A thorough assessment of the capability for a global climate model such as the Climate Forecast System Version 2 (CFSv2) to skillfully represent parameters related to severe weather has the potential to significantly improve medium- to long-range outlooks vital to risk managers. Environmental convective available potential energy (CAPE) and deep-layer vertical wind shear (DLS) can be used to distinguish an atmosphere conducive to severe storms from one supportive of primarily non-severe 'ordinary' convection. As such, this research concentrates on the predictability of CAPE, DLS, and a product of the two parameters (CAPEDLS) by the CFSv2 with a specific focus on the subseasonal timescale. Individual month-long verification periods from the Climate Forecast System reanalysis (CFSR) dataset are measured against a climatological standard using cumulative distribution function (CDF) and area-under-the-CDF (AUCDF) techniques designed mitigate inherent model biases while concurrently assessing the entire distribution of a given parameter in lieu of a threshold-based approach. Similar methods imposed upon the CFS reforecast (CFSRef) and operational CFSv2 allow for comparisons elucidating both spatial and temporal trends in skill using correlation coefficients, proportion correct metrics, Heidke skill score (HSS), and root-mean-square-error (RMSE) statistics. Key results show the CFSv2-based output often demonstrates skill beyond a climatologically-based threshold when the forecast is notably anomalous from the 29-year (1982-2010) mean CFSRef prediction (exceeding one standard deviation at grid point level). CFSRef analysis indicates enhanced skill during the months of April and June (relative to May) and for predictions of DLS. Furthermore, years exhibiting skill in terms of RMSE are shown to possess certain correlations with El Nino-Southern Oscillation conditions from the preceding winter and concurrent Madden Julian Oscillation activity. Applying results gleaned from the CFSRef analysis to the operational CFSv2 (2011-16) indicates predictive skill can be increased by isolating forecasts meeting multiple parameter-based relationships.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

NASA Astrophysics Data System (ADS)

Baehr, J.; Fröhlich, K.; Botzet, M.; Domeisen, D. I. V.; Kornblueh, L.; Notz, D.; Piontek, R.; Pohlmann, H.; Tietsche, S.; Müller, W. A.

2015-05-01

A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2-4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

Sensitivity of the Carolina Coastal Ocean Circulation to Open Boundary and Atmospheric Forcing

NASA Astrophysics Data System (ADS)

Liu, X.; Xie, L.; Pietrafesa, L.

2003-12-01

The ocean circulation on the continental shelf off the Carolina coast is characterized by a complex flow regime and temporal variability, which is influenced by atmospheric forcing, the Gulf Stream system, complex coastline and bathymetry, river discharge and tidal forcing. In this study, a triple-nested, HYbrid Coordinate Ocean Model (HYCOM) is used to simulate the coastal ocean circulation on the continental shelf off the Carolina coast and its interactions with the offshore large-scale ocean circulation system. The horizontal mesh size in the innermost domain was set to 1 km, whereas the outermost domain coincides with the near real-time 1/12­’ Atlantic HYCOM Nowcast/Forecast System operated at the Naval Research Laboratory. The intermediate domain uses a mesh size of 3 km. Atmospheric forcing fields for the Carolina coastal region are derived from the NOAA operational ETA model, the ECMWF reanalysis fields and NCEP/NCAR reanalysis fields. These forcing fields are derived at 0.8›¦, 1.125›¦ and 1.875›¦ resolutions, and at intervals of 6 hour, daily and monthly. The sensitivity of the model results to the spatial and temporal resolution of the atmospheric forcing fields is analyzed. To study the dependence of the model sensitivity on the model grid size, single-window simulations at resolutions of 1km, 3km and 9km are carried out using the same forcing fields that were applied to the nested system. Comparisons between the nested and the single domain simulation results will be presented.

Comparison of the ocean surface vector winds over the Nordic Seas and their application for ocean modeling

NASA Astrophysics Data System (ADS)

Dukhovskoy, Dmitry; Bourassa, Mark

2017-04-01

Ocean processes in the Nordic Seas and northern North Atlantic are strongly controlled by air-sea heat and momentum fluxes. The predominantly cyclonic, large-scale atmospheric circulation brings the deep ocean layer up to the surface preconditioning the convective sites in the Nordic Seas for deep convection. In winter, intensive cooling and possibly salt flux from newly formed sea ice erodes the near-surface stratification and the mixed layer merges with the deeper domed layer, exposing the very weakly stratified deep water mass to direct interaction with the atmosphere. Surface wind is one of the atmospheric parameters required for estimating momentum and turbulent heat fluxes to the sea ice and ocean surface. In the ocean models forced by atmospheric analysis, errors in surface wind fields result in errors in air-sea heat and momentum fluxes, water mass formation, ocean circulation, as well as volume and heat transport in the straits. The goal of the study is to assess discrepancies across the wind vector fields from reanalysis data sets and scatterometer-derived gridded products over the Nordic Seas and northern North Atlantic and to demonstrate possible implications of these differences for ocean modeling. The analyzed data sets include the reanalysis data from the National Center for Environmental Prediction Reanalysis 2 (NCEPR2), Climate Forecast System Reanalysis (CFSR), Arctic System Reanalysis (ASR) and satellite wind products Cross-Calibrated Multi-Platform (CCMP) wind product version 1.1 and recently released version 2.0, and Remote Sensing Systems QuikSCAT data. Large-scale and mesoscale characteristics of winds are compared at interannual, seasonal, and synoptic timescales. Numerical sensitivity experiments are conducted with a coupled ice-ocean model forced by different wind fields. The sensitivity experiments demonstrate differences in the net surface heat fluxes during storm events. Next, it is hypothesized that discrepancies in the wind vorticity fields should manifest different behaviors of the isopycnals in the Nordic Seas. Time evolution of isopycnal depths in the sensitivity experiments forced by different wind fields is discussed. Results of these sensitivity experiments demonstrate a relationship between the isopycnal surfaces and the wind stress curl. The numerical experiments are also analyzed to investigate the relationship between the East Greenland Current and the wind stress curl over the Nordic Seas. The transport of the current at this location has substantial contribution from wind-driven large-scale circulation. This wind-driven part of the East Greenland Current is a western-intensified return flow of a wind-driven cyclonic gyre in the central Nordic Seas. The numerical experiments with different wind fields reveal notable sensitivity of the East Greenland Current to differences in the wind forcing.

Impact of the springtime Himalayan-Tibetan Plateau on the onset on the Indian summer monsoon in coupled forecasts

NASA Astrophysics Data System (ADS)

Orsolini, Yvan; Senan, Retish; Weisheimer, Antje; Vitart, Frederic; Balsamo, Gianpaolo; Doblas-Reyes, Francisco; Stockdale, Timothy; Dutra, Emanuel

2016-04-01

The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region has long been suggested to be an influential factor on the onset of the Indian summer monsoon. In the frame of the SPECS project, we have assessed the impact of realistic snow initialization in springtime over HTP on the onset of the Indian summer monsoon. We examine a suite of coupled ocean-atmosphere 4-month ensemble reforecasts made at the European Centre for Medium-Range Weather Forecasts (ECMWF), using the Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (i) the onset is delayed by about 8 days, and (ii) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by either the El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

Using High Resolution Model Data to Improve Lightning Forecasts across Southern California

NASA Astrophysics Data System (ADS)

Capps, S. B.; Rolinski, T.

2014-12-01

Dry lightning often results in a significant amount of fire starts in areas where the vegetation is dry and continuous. Meteorologists from the USDA Forest Service Predictive Services' program in Riverside, California are tasked to provide southern and central California's fire agencies with fire potential outlooks. Logistic regression equations were developed by these meteorologists several years ago, which forecast probabilities of lightning as well as lightning amounts, out to seven days across southern California. These regression equations were developed using ten years of historical gridded data from the Global Forecast System (GFS) model on a coarse scale (0.5 degree resolution), correlated with historical lightning strike data. These equations do a reasonably good job of capturing a lightning episode (3-5 consecutive days or greater of lightning), but perform poorly regarding more detailed information such as exact location and amounts. It is postulated that the inadequacies in resolving the finer details of episodic lightning events is due to the coarse resolution of the GFS data, along with limited predictors. Stability parameters, such as the Lifted Index (LI), the Total Totals index (TT), Convective Available Potential Energy (CAPE), along with Precipitable Water (PW) are the only parameters being considered as predictors. It is hypothesized that the statistical forecasts will benefit from higher resolution data both in training and implementing the statistical model. We have dynamically downscaled NCEP FNL (Final) reanalysis data using the Weather Research and Forecasting model (WRF) to 3km spatial and hourly temporal resolution across a decade. This dataset will be used to evaluate the contribution to the success of the statistical model of additional predictors in higher vertical, spatial and temporal resolution. If successful, we will implement an operational dynamically downscaled GFS forecast product to generate predictors for the resulting statistical lightning model. This data will help fire agencies be better prepared to pre-deploy resources in advance of these events. Specific information regarding duration, amount, and location will be especially valuable.

IMPACT OF TRMM PRECIPITATION ON CPTEC’S RPSAS ANALYSIS

NASA Astrophysics Data System (ADS)

Herdies, D. L.; Bastarz, C. F.; Fernandez, J. P.

2009-12-01

In this work a data assimilation study was performed to assess the impact of estimated precipitation from TRMM (Tropical Rainfall Measuring Mission) on the CPTEC (Centro de Previsão de Tempo e Estudos Climáticos at Brasil) RPSAS (Regional Physical-space Statistical Analysis System) analyses and the Eta model forecast over the region of La Plata Basin, during a case o MCC (Mesoscale Convective Complex) occurred between 22th and 23th January 2003. The data assimilation system RPSAS and the mesoscale regional Eta model (both with 20km of spatial resolution) were run together with and without the TRMM precipitation. Is this study the assimilation of precipitation is basically a nudging process and is performed during the first guess stage by the Eta model, like in the NCEP (National Centers for Environmental Predictions) EDAS (Eta Data Assimilation System) precipitation data assimilation. During this process the model adjusts the precipitation by comparing, at which grid point and at which time step, the model precipitation against the TRMM precipitation. Doing this some adjustments are made on the latent heat vertical profile, water vapor mixing ratio and relative humidity, by considering the Betts-Miller-Janjic convective parameterization. On the next step, the RPSAS produces an analysis which covers most of the South America and the adjacent oceans. From this analysis the Eta model produces 6h, 12h, 18h and 24h forecast. Data collected from the SALLJEX (South America Low Level Jet EXperiment) was used to compare the forecasts of the model and the CPTEC 40km Regional Reanalysis was used to compare with the RPSAS analyses. Some preliminary results show that the precipitation assimilation improves the first hours of the forecast (typically 6h). The variables verified were the zonal and meridional wind, geopotential height and the precipitation. The convective precipitation fields were improved, mainly over the 6h forecast. This is an important improvement because the first guess field will serve as an analysis of the next forecast window. Also were noticed that the mean error for those variables was reduced (principally for the zonal wind). This reveals that with an improved first guess field, the model was able to detect the MCC occurred in the north of Argentina, due to the improved representation of the winds fields (direction and intensity), pressure and the surface variables.

Snow Accumulation Variability Over the West Antarctic Ice Sheet Since 1900: A Comparison of Ice Core Records With ERA-20C Reanalysis

NASA Astrophysics Data System (ADS)

Wang, Yetang; Thomas, Elizabeth R.; Hou, Shugui; Huai, Baojuan; Wu, Shuangye; Sun, Weijun; Qi, Shanzhong; Ding, Minghu; Zhang, Yulun

2017-11-01

This study uses a set of 37 firn core records over the West Antarctic Ice Sheet (WAIS) to test the performance of the twentieth century from the European Centre for Medium-Range Weather Forecasts (ERA-20C) reanalysis for snow accumulation and quantify temporal variability in snow accumulation since 1900. The firn cores are allocated to four geographical areas demarcated by drainage divides (i.e., Antarctic Peninsula (AP), western WAIS, central WAIS, and eastern WAIS) to calculate stacked records of regional snow accumulation. Our results show that the interannual variability in ERA-20C precipitation minus evaporation (P - E) agrees well with the corresponding ice core snow accumulation composites in each of the four geographical regions, suggesting its skill for simulating snow accumulation changes before the modern satellite era (pre-1979). Snow accumulation experiences significantly positive trends for the AP and eastern WAIS, a negative trend for the western WAIS, and no significant trend for the central WAIS from 1900 to 2010. The contrasting trends are associated with changes in the large-scale moisture transport driven by a deepening of the low-pressure systems and anomalies of sea ice in the Amundsen Sea Low region.

Has upwelling strengthened along worldwide coasts over 1982-2010?

NASA Astrophysics Data System (ADS)

Varela, R.; Álvarez, I.; Santos, F.; Decastro, M.; Gómez-Gesteira, M.

2015-05-01

Changes in coastal upwelling strength have been widely studied since 1990 when Bakun proposed that global warming can induce the intensification of upwelling in coastal areas. Whether present wind trends support this hypothesis remains controversial, as results of previous studies seem to depend on the study area, the length of the time series, the season, and even the database used. In this study, temporal and spatial trends in the coastal upwelling regime worldwide were investigated during upwelling seasons from 1982 to 2010 using a single wind database (Climate Forecast System Reanalysis) with high spatial resolution (0.3°). Of the major upwelling systems, increasing trends were only observed in the coastal areas of Benguela, Peru, Canary, and northern California. A tendency for an increase in upwelling-favourable winds was also identified along several less studied regions, such as the western Australian and southern Caribbean coasts.

Has upwelling strengthened along worldwide coasts over 1982-2010?

PubMed Central

Varela, R.; Álvarez, I.; Santos, F.;  deCastro, M.; Gómez-Gesteira, M.

2015-01-01

Changes in coastal upwelling strength have been widely studied since 1990 when Bakun proposed that global warming can induce the intensification of upwelling in coastal areas. Whether present wind trends support this hypothesis remains controversial, as results of previous studies seem to depend on the study area, the length of the time series, the season, and even the database used. In this study, temporal and spatial trends in the coastal upwelling regime worldwide were investigated during upwelling seasons from 1982 to 2010 using a single wind database (Climate Forecast System Reanalysis) with high spatial resolution (0.3°). Of the major upwelling systems, increasing trends were only observed in the coastal areas of Benguela, Peru, Canary, and northern California. A tendency for an increase in upwelling-favourable winds was also identified along several less studied regions, such as the western Australian and southern Caribbean coasts. PMID:25952477

Comparison of surface freshwater fluxes from different climate forecasts produced through different ensemble generation schemes.

NASA Astrophysics Data System (ADS)

Romanova, Vanya; Hense, Andreas; Wahl, Sabrina; Brune, Sebastian; Baehr, Johanna

2016-04-01

The decadal variability and its predictability of the surface net freshwater fluxes is compared in a set of retrospective predictions, all using the same model setup, and only differing in the implemented ocean initialisation method and ensemble generation method. The basic aim is to deduce the differences between the initialization/ensemble generation methods in view of the uncertainty of the verifying observational data sets. The analysis will give an approximation of the uncertainties of the net freshwater fluxes, which up to now appear to be one of the most uncertain products in observational data and model outputs. All ensemble generation methods are implemented into the MPI-ESM earth system model in the framework of the ongoing MiKlip project (www.fona-miklip.de). Hindcast experiments are initialised annually between 2000-2004, and from each start year 10 ensemble members are initialized for 5 years each. Four different ensemble generation methods are compared: (i) a method based on the Anomaly Transform method (Romanova and Hense, 2015) in which the initial oceanic perturbations represent orthogonal and balanced anomaly structures in space and time and between the variables taken from a control run, (ii) one-day-lagged ocean states from the MPI-ESM-LR baseline system (iii) one-day-lagged of ocean and atmospheric states with preceding full-field nudging to re-analysis in both the atmospheric and the oceanic component of the system - the baseline one MPI-ESM-LR system, (iv) an Ensemble Kalman Filter (EnKF) implemented into oceanic part of MPI-ESM (Brune et al. 2015), assimilating monthly subsurface oceanic temperature and salinity (EN3) using the Parallel Data Assimilation Framework (PDAF). The hindcasts are evaluated probabilistically using fresh water flux data sets from four different reanalysis data sets: MERRA, NCEP-R1, GFDL ocean reanalysis and GECCO2. The assessments show no clear differences in the evaluations scores on regional scales. However, on the global scale the physically motivated methods (i) and (iv) provide probabilistic hindcasts with a consistently higher reliability than the lagged initialization methods (ii)/(iii) despite the large uncertainties in the verifying observations and in the simulations.

Assessment of WRF Simulated Precipitation by Meteorological Regimes

NASA Astrophysics Data System (ADS)

Hagenhoff, Brooke Anne

This study evaluated warm-season precipitation events in a multi-year (2007-2014) database of Weather Research and Forecasting (WRF) simulations over the Northern Plains and Southern Great Plains. These WRF simulations were run daily in support of the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) by the National Severe Storms Laboratory (NSSL) for operational forecasts. Evaluating model skill by synoptic pattern allows for an understanding of how model performance varies with particular atmospheric states and will aid forecasters with pattern recognition. To conduct this analysis, a competitive neural network known as the Self-Organizing Map (SOM) was used. SOMs allow the user to represent atmospheric patterns in an array of nodes that represent a continuum of synoptic categorizations. North American Regional Reanalysis (NARR) data during the warm season (April-September) was used to perform the synoptic typing over the study domains. Simulated precipitation was evaluated against observations provided by the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis.

Improving Timeliness of Winter Wheat Production Forecast in United States of America, Ukraine and China Using MODIS Data and NCAR Growing Degree Day

NASA Astrophysics Data System (ADS)

Vermote, E.; Franch, B.; Becker-Reshef, I.; Claverie, M.; Huang, J.; Zhang, J.; Sobrino, J. A.

2014-12-01

Wheat is the most important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely forecasts of its production are critical for informing agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) used an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel, and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: United States of America, Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest) while conserving an accuracy of 10% in the production forecast.

How well do we know the global water cycle? - Intercomparison and Performance Analysis of the Hydrological Cycle in Three State-of-the-Art Reanalyses

NASA Astrophysics Data System (ADS)

Kunstmann, H.; Lorenz, C.

2012-12-01

The three state-of-the-art global atmospheric reanalysis models—namely, ECMWF Interim Re-Analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA; NASA), and Climate Forecast System Reanalysis (CFSR; NCEP)—are analyzed and compared with independent observations (GPCC; GPCP; CRU; CPC; DEL; HOAPS) in the period between 1989 and 2006. Comparison of precipitation and temperature estimates from the three models with gridded observations reveals large differences between the reanalyses and also of the observation datasets. A major source of uncertainty in the observations is the spatial distribution and change of the number of gauges over time. In South America for example, active measuring stations were reduced from 4267 to 390. The quality of precipitation estimates from the reanalyses strongly depends on the geographic location, as there are significant differences especially in tropical regions. The closure of the water cycle in the three reanalyses is analyzed by estimating long-term mean values for precipitation, evapotranspiration, surface runoff, and moisture flux divergence. Major shortcomings in the moisture budgets of the datasets are mainly due to inconsistencies of the net precipitation minus evaporation and evapotranspiration, respectively, (P-E) estimates over the oceans and landmasses. This imbalance largely originates from the assimilation of radiance sounding data from the NOAA-15 satellite, which results in an unrealistic increase of oceanic P-E in the MERRA and CFSR budgets. Overall, ERA-Interim shows both a comparatively reasonable closure of the terrestrial and atmospheric water balance and a reasonable agreement with the observation datasets. The limited performance of the three state-of-the-art reanalyses in reproducing the hydrological cycle, however, puts the use of these models for climate trend analyses and long-term water budget studies into question.

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this downscaling approach considering perfect-model conditions, but we will also analyze the suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE PAGES

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; ...

2016-03-16

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

GEOS S2S-2_1 File Specification: GMAO Seasonal and Sub-Seasonal Forecast Output

NASA Technical Reports Server (NTRS)

Kovach, Robin M.; Marshak, Jelena; Molod, Andrea; Nakada, Kazumi

2018-01-01

The NASA GMAO seasonal (9 months) and subseasonal (45 days) forecasts are produced with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2_1. The new system replaces version S2S-1.0 described in Borovikov et al (2017), and includes upgrades to many components of the system. The atmospheric model includes an upgrade from a pre-MERRA-2 version running on a latitude-longitude grid at approx. 1 degree resolution to a current version running on a cubed sphere grid at approximately 1/2 degree resolution. The important developments are related to the dynamical core (Putman et al., 2011), the moist physics (''two-moment microphysics'' of Barahona et al., 2014) and the cryosphere (Cullather et al., 2014). As in the previous GMAO S2S system, the land model is that of Koster et al (2000). GMAO S2S-2_1 now includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART, Colarco et al., 2010) single moment interactive aerosol model that includes predictive aerosols including dust, sea salt and several species of carbon and sulfate. The previous version of GMAO S2S specified aerosol amounts from climatology, which were used to inform the atmospheric radiation only. The ocean model includes an upgrade from MOM4 to MOM5 (Griffies 2012), and continues to be run on the tripolar grid at approximately 1/2 degree resolution in the tropics with 40 vertical levels. As in S2S-1.0, the sea ice model is from the Los Alamos Sea Ice model (CICE4, Hunke and Lipscomb 2010). The Ocean Data Assimilation System (ODAS) has been upgraded from the one described in Borovikov et al., 2017 to one that uses a modified version of the Penny, 2014 Local Ensemble Transform Kalman Filter (LETKF), and now assimilates along-track altimetry. The ODAS also does a nudging to MERRA-2 SST and sea ice boundary conditions. The atmospheric data assimilation fields used to constrain the atmosphere in the ODAS have been upgraded from MERRA to a MERRA-2 like system. The system is initialized using a MERRA-2-like atmospheric reanalysis (Gelaro et al. 2017) and the GMAO S2S-2_1 ocean analysis. Additional ensemble members for forecasts are produced with initial states at 5-day intervals, with additional members based on perturbations of the atmospheric and ocean states. Both subseasonal and seasonal forecasts are submitted to the National MultiModel Ensemble (NMME) project, and are part of the US/Canada multimodel seasonal forecasts (http://www.cpc.ncep.noaa.gov/products/NMME/). A large suite of retrospective forecasts (''hindcasts'') have been completed, and contribute to the calculation of the model's baseline climatology and drift, anomalies from which are the basis of the seasonal forecasts.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 3. Evaluation by means of case studies

NASA Astrophysics Data System (ADS)

Mangold, A.; de Backer, H.; de Paepe, B.; Dewitte, S.; Chiapello, I.; Derimian, Y.; Kacenelenbogen, M.; LéOn, J.-F.; Huneeus, N.; Schulz, M.; Ceburnis, D.; O'Dowd, C.; Flentje, H.; Kinne, S.; Benedetti, A.; Morcrette, J.-J.; Boucher, O.

2011-02-01

A near real-time system for assimilation and forecasts of aerosols, greenhouse and trace gases, extending the ECMWF Integrated Forecasting System (IFS), has been developed in the framework of the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The GEMS aerosol modeling system is novel as it is the first aerosol model fully coupled to a numerical weather prediction model with data assimilation. A reanalysis of the period 2003-2009 has been carried out with the same system. During its development phase, the aerosol system was first run for the time period January 2003 to December 2004 and included sea salt, desert dust, organic matter, black carbon, and sulfate aerosols. In the analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) at 550 nm over ocean and land (except over bright surfaces) was assimilated. This work evaluates the performance of the aerosol system by means of case studies. The case studies include (1) the summer heat wave in Europe in August 2003, characterized by forest fire aerosol and conditions of high temperatures and stagnation, favoring photochemistry and secondary aerosol formation, (2) a large Saharan dust event in March 2004, and (3) periods of high and low sea salt aerosol production. During the heat wave period in 2003, the linear correlation coefficients between modeled and observed AOD (550 nm) and between modeled and observed PM2.5 mass concentrations are 0.82 and 0.71, respectively, for all investigated sites together. The AOD is slightly and the PM2.5 mass concentration is clearly overestimated by the aerosol model during this period. The simulated sulfate mass concentration is significantly correlated with observations but is distinctly overestimated. The horizontal and vertical locations of the main features of the aerosol distribution during the Saharan dust outbreak are generally well captured, as well as the timing of the AOD peaks. The aerosol model simulates winter sea salt AOD reasonably well, however, showing a general overestimation. Summer sea salt events show a better agreement. Overall, the assimilation of MODIS AOD data improves the subsequent aerosol predictions when compared with observations, in particular concerning the correlation and AOD peak values. The assimilation is less effective in correcting a positive (PM2.5, sulfate mass concentration, Angström exponent) or negative (desert dust plume AOD) model bias.

MERRA-2 Input Observations: Summary and Assessment

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); McCarty, Will; Coy, Lawrence; Gelaro, Ronald; Huang, Albert; Merkova, Dagmar; Smith, Edmond B.; Sienkiewicz, Meta; Wargan, Krzysztof

2016-01-01

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis, spanning 1980 through near-realtime, that uses state-of-the-art processing of observations from the continually evolving global observing system. The effectiveness of any reanalysis is a function not only of the input observations themselves, but also of how the observations are handled in the assimilation procedure. Relevant issues to consider include, but are not limited to, data selection, data preprocessing, quality control, bias correction procedures, and blacklisting. As the assimilation algorithm and earth system models are fundamentally fixed in a reanalysis, it is often a change in the character of the observations, and their feedbacks on the system, that cause changes in the character of the reanalysis. It is therefore important to provide documentation of the observing system so that its discontinuities and transitions can be readily linked to discontinuities seen in the gridded atmospheric fields of the reanalysis. With this in mind, this document provides an exhaustive list of the input observations, the context under which they are assimilated, and an initial assessment of selected core observations fundamental to the reanalysis.

A First Look at the Structure of the Wave Pouch during the 2009 PREDICT-GRIP Dry Runs over the Atlantic

DTIC Science & Technology

2012-04-01

for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data and the satellite brightness temperature between 1979 and 2001, Hopsch et al. (2010...Zipser (2009) screened out disturbances lacking cold cloud-top areas in the infrared (IR) satellite data . Despite all of these analyses, the essential...paper we use the analysis and satellite data collected during the 2009 Atlantic hurricane season to examine the kinematic, dynamic, and thermodynamic

A Multi-Scale Analysis of Tropical Cyclogenesis Within the Critical Layer of Tropical Easterly Waves in the Atlantic and Western North Pacific Sectors

DTIC Science & Technology

2010-09-01

Electra Doppler Radar (ELDORA), dropwindsonde capability, a Doppler wind lidar , and the ability to collect flight-level data] flew aircraft research...ELDORA Electra Doppler Radar ECMWF European Center for Medium-range Weather Prediction Forecasts ER Equatorial Rossby ERA-40 ECMWF Reanalysis Data...2006) use Dual Doppler radar and rain gauge data to evaluate the performance of the TRMM TMI V6 rainfall algorithm. They 23 conclude that: “In

Evaluating Observation Influence on Regional Water Budgets in Reanalyses

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Chern, Jiun-Dar; Mocko, David; Robertson, Franklin R.; daSilva, Arlindo M.

2014-01-01

The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model, or perhaps inconsistencies in the observing system and its assimilation. In the MERRA reanalysis, an area of long term moisture flux divergence over land has been identified over the Central United States. Here, we evaluate the water vapor budget in this region, taking advantage of two unique features of the MERRA diagnostic output; 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output data set of the assimilated observations and their innovations (e.g. forecast departures). In the Central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRAs Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 06Z and 18Z analysis cycles. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSUA (mainly window channels) and AIRS. This effort also shows the complexities of the observing system, and the reactions of the regional water budgets in reanalyses to the assimilated observations.

Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

NASA Astrophysics Data System (ADS)

Dominguez, F.; Castro, C. L.

2009-12-01

The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.

A low-order model for long-range infrasound propagation in random atmospheric waveguides

NASA Astrophysics Data System (ADS)

Millet, C.; Lott, F.

2014-12-01

In numerical modeling of long-range infrasound propagation in the atmosphere, the wind and temperature profiles are usually obtained as a result of matching atmospheric models to empirical data. The atmospheric models are classically obtained from operational numerical weather prediction centers (NOAA Global Forecast System or ECMWF Integrated Forecast system) as well as atmospheric climate reanalysis activities and thus, do not explicitly resolve atmospheric gravity waves (GWs). The GWs are generally too small to be represented in Global Circulation Models, and their effects on the resolved scales need to be parameterized in order to account for fine-scale atmospheric inhomogeneities (for length scales less than 100 km). In the present approach, the sound speed profiles are considered as random functions, obtained by superimposing a stochastic GW field on the ECMWF reanalysis ERA-Interim. The spectral domain is binned by a large number of monochromatic GWs, and the breaking of each GW is treated independently from the others. The wave equation is solved using a reduced-order model, starting from the classical normal mode technique. We focus on the asymptotic behavior of the transmitted waves in the weakly heterogeneous regime (for which the coupling between the wave and the medium is weak), with a fixed number of propagating modes that can be obtained by rearranging the eigenvalues by decreasing Sobol indices. The most important feature of the stochastic approach lies in the fact that the model order (i.e. the number of relevant eigenvalues) can be computed to satisfy a given statistical accuracy whatever the frequency. As the low-order model preserves the overall structure of waveforms under sufficiently small perturbations of the profile, it can be applied to sensitivity analysis and uncertainty quantification. The gain in CPU cost provided by the low-order model is essential for extracting statistical information from simulations. The statistics of a transmitted broadband pulse are computed by decomposing the original pulse into a sum of modal pulses that propagate with different phase speeds and can be described by a front pulse stabilization theory. The method is illustrated on two large-scale infrasound calibration experiments, that were conducted at the Sayarim Military Range, Israel, in 2009 and 2011.

The PEcAn Project: Accessible Tools for On-demand Ecosystem Modeling

NASA Astrophysics Data System (ADS)

Cowdery, E.; Kooper, R.; LeBauer, D.; Desai, A. R.; Mantooth, J.; Dietze, M.

2014-12-01

Ecosystem models play a critical role in understanding the terrestrial biosphere and forecasting changes in the carbon cycle, however current forecasts have considerable uncertainty. The amount of data being collected and produced is increasing on daily basis as we enter the "big data" era, but only a fraction of this data is being used to constrain models. Until we can improve the problems of model accessibility and model-data communication, none of these resources can be used to their full potential. The Predictive Ecosystem Analyzer (PEcAn) is an ecoinformatics toolbox and a set of workflows that wrap around an ecosystem model and manage the flow of information in and out of regional-scale TBMs. Here we present new modules developed in PEcAn to manage the processing of meteorological data, one of the primary driver dependencies for ecosystem models. The module downloads, reads, extracts, and converts meteorological observations to Unidata Climate Forecast (CF) NetCDF community standard, a convention used for most climate forecast and weather models. The module also automates the conversion from NetCDF to model specific formats, including basic merging, gap-filling, and downscaling procedures. PEcAn currently supports tower-based micrometeorological observations at Ameriflux and FluxNET sites, site-level CSV-formatted data, and regional and global reanalysis products such as the North American Regional Reanalysis and CRU-NCEP. The workflow is easily extensible to additional products and processing algorithms.These meteorological workflows have been coupled with the PEcAn web interface and now allow anyone to run multiple ecosystem models for any location on the Earth by simply clicking on an intuitive Google-map based interface. This will allow users to more readily compare models to observations at those sites, leading to better calibration and validation. Current work is extending these workflows to also process field, remotely-sensed, and historical observations of vegetation composition and structure. The processing of heterogeneous met and veg data within PEcAn is made possible using the Brown Dog cyberinfrastructure tools for unstructured data.

Evaluating Carbon Isotope Signature of Bulk Organic Matter and Plant Wax Derived n-alkanes from Lacustrine Sediments as Climate Proxies along the Western Side of the Andes

NASA Astrophysics Data System (ADS)

Contreras, S.; Werne, J. P.; Araneda, A.; Conejero, C. A.

2015-12-01

Sedimentary carbon isotope values (δ13C) of bulk organic matter and long chain (C25 to C35) n-alkanes are among the most long-lived and widely utilized proxies of organic matter and vegetation source. The carbon distribution (e.g. average carbon chain length, ACL) and isotope signature from long chain n-alkanes had been intensively used on paleoclimate studies because they are less influenced by diagenesis, differential preservation of compound classes, and changes in the sources of organic matter than bulk δ13C values. Recently, studies of modern plant n-alkanes have challenged the use of carbon distribution and carbon isotope signature from sedimentary n-alkanes as reliable indicators of vegetation and climate change. The climate in central-south western South America (SA) is projected to become significantly warmer and drier over the next several decades to centuries in response to anthropogenically driven warming. Paleolimnological studies along western SA are critical to obtain more realistic and reliable regional reconstructions of past climate and environments, including vegetation and water budget variability. Here we discuss bulk δ13C, distribution and δ13C in long chain n-alkanes from a suite of ~40 lake surface sediment (core-top) samples spanning the transition from a Mediterranean climate with a patchwork of cultivated vegetation, pastureland, conifers in central Chile to a rainy temperate climate dominated by broadleaf deciduous and evergreen forest. Data are compared to the latitudinal and orographic climatic trends of the Andes based on the climatology (e.g. precipitation and temperature) of the locations of all lakes involved in this study, using monthly gridded reanalysis products of the Climate Forecast System Reanalysis (CFSR), based on the NCEP global forecast model and meteorological stations available in the region, from January 1979 to December 2010 with a 0.5° horizontal resolution.

Coastal Low-Level Wind Jets: A Global Study Based On An Ensemble Of Reanalysis

NASA Astrophysics Data System (ADS)

Cardoso, R. M.; Lima, D. C. A.; Soares, P. M. M.; Semedo, A.

2017-12-01

Reanalyses data are a useful tool for climate and atmospheric studies since they provide physically consistent spatial and temporal information of observable and unobservable atmospheric parameters. Here, we propose the analysis of coastal low-level jets (CLLJs) resorting to three global reanalyses. The six hourly data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), the Japanese 55-year Reanalysis (JRA-55) and the Modern Era Retrospective-analysis for Research and Applications (MERRA2), are used to build an ensemble of reanalyses, for a period encompassing 1980-2016. A detailed global climatology of CLLJs is presented based on a reanalyses ensemble. This gives robustness to the CLLJs representation and also reduces uncertainty. The annual and diurnal cycle as well as the inter-annual variability are analysed in order to evaluate the temporal fluctuations of frequency of occurrence of CLLJ. The ensemble mean displays a good representation of their seasonal spatial variability. The Oman and Benguela CLLJs show, respectively, a decrease and increase of frequency of occurrence, which is statistically significant during boreal summer and austral spring for the period of study. The Oman CLLJ is the most intense and occurs in higher altitudes when compared with the other jets occurring during the season where each CLLJs have higher mean incidence.

New Developments in the Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Hoar, T. J.; Anderson, J. L.; Raeder, K.; Karspeck, A. R.; Romine, G.; Liu, H.; Collins, N.

2011-12-01

NCAR's Data Assimilation Research Testbed (DART) is a community facility that provides ensemble data assimilation tools for geophysical applications. DART works with an expanding set of models and a wide range of conventional and novel observations, and provides a variety of assimilation algorithms and diagnostic tools. The Kodiak release of DART became available in July 2011 and includes more than 20 major feature enhancements, support for 24 models, support for (at least) 14 observation formats, expanded documentation and diagnostic tools, and 12 new utilities. A few examples of research projects that demonstrate the effectiveness and flexibility of the DART are described. The Community Atmosphere Model (CAM) and DART assimilated all the observations that were used in the NCEP/NCAR Reanalysis to produce a global, 6-hourly, 80-member ensemble reanalysis for 1998 through the present. The dataset is ideal for research applications that would benefit from an ensemble of equally-likely atmospheric states that are consistent with observations. Individual ensemble members may be used as a "data atmosphere" in any Community Earth System Model (CESM) experiment. The CESM interfaces for the Parallel Ocean Program (POP) and the Community Land Model (CLM) also support multiple instances, allowing data assimilation experiments exploiting unique atmospheric forcing for each POP or CLM model instance. A multi-year DART ocean assimilation has been completed and provides valuable insight into the successes and challenges of oceanic data assimilation. The DART/CLM research focuses on snow cover fraction and snow depth. The Weather Research and Forecasting (WRF) model was used with DART to perform a real-time CONUS domain mesoscale ensemble analysis with continuous cycling for 47 days. A member was selected once daily for high-resolution convective forecasts supporting a test phase of the Deep Convective Clouds and Chemistry experiment and the Storm Prediction Center spring experiment. The impacts of Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer (AMSR) microwave total precipitable water (TPW) observations on analyses and forecasts of tropical cyclone Sinlaku (2008) are investigated by performing assimilations with a 45km resolution WRF model over the Western Pacific domain for 8-14 Septmber, 2008. Particular emphasis is on the performance of the assimilation algorithms in the hurricane core and the impact of novel observations in the hurricane core.

The End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

NASA Astrophysics Data System (ADS)

Wood, Eric; Wanders, Niko; Pan, Ming; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohinni; Prudhomme, Christel; Houghton-Carr, Helen

2017-04-01

High-resolution simulations of water resources from hydrological models are vital to supporting important climate services. Apart from a high level of detail, both spatially and temporally, it is important to provide simulations that consistently cover a range of timescales, from historical reanalysis to seasonal forecast and future projections. In the new EDgE project commissioned by the ECMWF (C3S) we try to fulfill these requirements. EDgE is a proof-of-concept project which combines climate data and state-of-the-art hydrological modelling to demonstrate a water-oriented information system implemented through a web application. EDgE is working with key European stakeholders representative of private and public sectors to jointly develop and tailor approaches and techniques. With these tools, stakeholders are assisted in using improved climate information in decision-making, and supported in the development of climate change adaptation and mitigation policies. Here, we present the first results of the EDgE modelling chain, which is divided into three main processes: 1) pre-processing and downscaling; 2) hydrological modelling; 3) post-processing. Consistent downscaling and bias corrections for historical simulations, seasonal forecasts and climate projections ensure that the results across scales are robust. The daily temporal resolution and 5km spatial resolution ensure locally relevant simulations. With the use of four hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), uncertainty between models is properly addressed, while consistency is guaranteed by using identical input data for static land surface parameterizations. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been created in collaboration with the end-user community of the EDgE project. The final product of this project is composed of 15 years of seasonal forecast and 10 climate change projections, all combined with four hydrological models. These unique high-resolution climate information simulations in the EDgE project provide an unprecedented information system for decision-making over Europe.

Evaluation of the ability of the MACC-II Reanalysis to reproduce the distribution of O3 and CO in the UTLS as measured by MOZAIC-IAGOS

NASA Astrophysics Data System (ADS)

Gaudel, A.; Clark, H.; Thouret, V.; Eskes, H.; Huijnen, V.; Nedelec, P.

2013-12-01

Tropospheric ozone is probably one of the most important trace gases in the atmosphere. It plays a major role in the chemistry of the troposphere by exerting a strong influence on the concentrations of oxidants such as hydroxyl radical (OH) and is the third greenhouse gas after carbon dioxide and methane. Its radiative impact is of particular importance in the Upper Troposphere / Lower Stratosphere (UTLS), the most critical region regarding the climate change. Carbon Monoxide (CO) is one of the major ozone precursors (originating from all types of combustion) in the troposphere. In the UTLS, it also has implications for stratospheric chemistry and indirect radiative forcing effects (as a chemical precursor of CO2 and O3). Assessing the global distribution (and possibly trends) of O3 and CO in this region of the atmosphere, combining high resolution in situ data and the most appropriate global 3D model to further quantify the different sources and their origins is then of particular interest. This is one of the objectives of the MOZAIC-IAGOS (http://www.iagos.fr) and MACC-II (http://www.gmes-atmosphere.eu) European programs. The aircraft of the MOZAIC program have collected simultaneously O3 and CO data regularly all over the world since the end of 2001. Most of the data are recorded in northern mid-latitudes, in the UTLS region (as commercial aircraft cruise altitude is between 9 and 12 km). MACC-II aims at providing information services covering air quality, climate forcing and stratospheric ozone, UV radiation and solar-energy resources, using near real time analysis and forecasting products, and reanalysis. The validation reports of the MACC models are regularly published (http://www.gmes-atmosphere.eu/services/gac/nrt/ and http://www.gmes-atmosphere.eu/services/gac/reanalysis/). We will present and discuss the performance of the MACC-reanalysis, including the ECMWF-Integrated Forecasting System (IFS) coupled to the CTM MOZART with 4DVAR data assimilation, to reproduce ozone and CO in the UTLS, as evaluated by the observations of MOZAIC between 2003 and 2008. In the UT, the model tends to overestimate O3 by about 30-40 % in the mid-latitudes and polar regions. This applies broadly to all seasons but is more marked in DJF and MAM. In tropical regions, the model underestimates UT ozone by about 20 % in all seasons but this is stronger in JJA. Upper-tropospheric CO is globally underestimated by the model in all seasons, by 10-20 %. In the southern hemisphere, it is particularly the case in SON in the regions of wildfires in South Africa. In the northern hemisphere, the zonal gradient of CO between the US, Europe and Asia is not well-captured by the model, especially in MAM.

Towards Experimental Operational Fire Weather Prediction at Subseasonal to Seasonal Scales for Alaska Using the NMME Hindcasts and Realtime Forecasts.

NASA Astrophysics Data System (ADS)

Sampath, A.; Bhatt, U. S.; Bieniek, P.; York, A.; Peng, P.; Brettschneider, B.; Thoman, R.; Jandt, R.; Ziel, R.; Branson, G.; Strader, M. H.; Alden, M. S.

2017-12-01

The summer 2004 and 2015 wildfires in Alaska were the two largest fire seasons on record since 1950 where approximately the land area of Massachusetts burned. The record fire year of 2004 resulted in 6.5 million acres burned while the 2015 wildfire season resulted in 5.2 million acres burned. In addition to the logistical cost of fighting fires and the loss of infrastructure, wildfires also lead to dangerous air quality in Alaska. Fires in Alaska result from lightning strikes coupled with persistent (extreme) dry warm conditions in remote areas with limited fire management and the seasonal climate/weather determine the extent of the fire season in Alaska. Advanced weather/climate outlooks for allocating staff and resources from days to a season are particularly needed by fire managers. However, there are no operational seasonal products currently for the Alaska region. Probabilistic forecasts of the expected seasonal climate/weather would aid tremendously in the planning process. Earlier insight of both lightening and fuel conditions would assist fire managers in planning resource allocation for the upcoming season. For fuel conditions, the state-of-the-art NMME (1982-2017) climate predictions were used to compute the Canadian Forest Fire Weather Index System (CFFWIS). The CFFWIS is used by fire managers to forecast forest fires in Alaska. NMME forecast (March and May) based Buildup Index (BUI) values were underestimated compared to BUI based on reanalysis and station data, demonstrating the necessity for bias correction. Post processing of NMME data will include bias correction using the quantile mapping technique. This study will provide guidance as to the what are the best available products for anticipating the fire season.

A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.

2017-12-01

Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

Comparison of present global reanalysis datasets in the context of a statistical downscaling method for precipitation prediction

NASA Astrophysics Data System (ADS)

Horton, Pascal; Weingartner, Rolf; Brönnimann, Stefan

2017-04-01

The analogue method is a statistical downscaling method for precipitation prediction. It uses similarity in terms of synoptic-scale predictors with situations in the past in order to provide a probabilistic prediction for the day of interest. It has been used for decades in a context of weather or flood forecasting, and is more recently also applied to climate studies, whether for reconstruction of past weather conditions or future climate impact studies. In order to evaluate the relationship between synoptic scale predictors and the local weather variable of interest, e.g. precipitation, reanalysis datasets are necessary. Nowadays, the number of available reanalysis datasets increases. These are generated by different atmospheric models with different assimilation techniques and offer various spatial and temporal resolutions. A major difference between these datasets is also the length of the archive they provide. While some datasets start at the beginning of the satellite era (1980) and assimilate these data, others aim at homogeneity on a longer period (e.g. 20th century) and only assimilate conventional observations. The context of the application of analogue methods might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies, a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. In order to fill this gap and to advise on the choice of appropriate datasets, nine different global reanalysis datasets were compared in seven distinct versions of analogue methods, over 300 precipitation stations in Switzerland. Significant differences in terms of prediction performance were identified. Although the impact of the reanalysis dataset on the skill score varies according to the chosen predictor, be it atmospheric circulation or thermodynamic variables, some hierarchy between the datasets is often preserved. This work can thus help choosing an appropriate dataset for the analogue method, or raise awareness of the consequences of using a certain dataset.

Verifying Operational and Developmental Air Force Weather Cloud Analysis and Forecast Products Using Lidar Data from Department of Energy Atmospheric Radiation Measurement (ARM) Sites

NASA Astrophysics Data System (ADS)

Hildebrand, E. P.

2017-12-01

Air Force Weather has developed various cloud analysis and forecast products designed to support global Department of Defense (DoD) missions. A World-Wide Merged Cloud Analysis (WWMCA) and short term Advected Cloud (ADVCLD) forecast is generated hourly using data from 16 geostationary and polar-orbiting satellites. Additionally, WWMCA and Numerical Weather Prediction (NWP) data are used in a statistical long-term (out to five days) cloud forecast model known as the Diagnostic Cloud Forecast (DCF). The WWMCA and ADVCLD are generated on the same polar stereographic 24 km grid for each hemisphere, whereas the DCF is generated on the same grid as its parent NWP model. When verifying the cloud forecast models, the goal is to understand not only the ability to detect cloud, but also the ability to assign it to the correct vertical layer. ADVCLD and DCF forecasts traditionally have been verified using WWMCA data as truth, but this might over-inflate the performance of those models because WWMCA also is a primary input dataset for those models. Because of this, in recent years, a WWMCA Reanalysis product has been developed, but this too is not a fully independent dataset. This year, work has been done to incorporate data from external, independent sources to verify not only the cloud forecast products, but the WWMCA data itself. One such dataset that has been useful for examining the 3-D performance of the cloud analysis and forecast models is Atmospheric Radiation Measurement (ARM) data from various sites around the globe. This presentation will focus on the use of the Department of Energy (DoE) ARM data to verify Air Force Weather cloud analysis and forecast products. Results will be presented to show relative strengths and weaknesses of the analyses and forecasts.

A data-assimilative ocean forecasting system for the Prince William sound and an evaluation of its performance during sound Predictions 2009

NASA Astrophysics Data System (ADS)

Farrara, John D.; Chao, Yi; Li, Zhijin; Wang, Xiaochun; Jin, Xin; Zhang, Hongchun; Li, Peggy; Vu, Quoc; Olsson, Peter Q.; Schoch, G. Carl; Halverson, Mark; Moline, Mark A.; Ohlmann, Carter; Johnson, Mark; McWilliams, James C.; Colas, Francois A.

2013-07-01

The development and implementation of a three-dimensional ocean modeling system for the Prince William Sound (PWS) is described. The system consists of a regional ocean model component (ROMS) forced by output from a regional atmospheric model component (the Weather Research and Forecasting Model, WRF). The ROMS ocean model component has a horizontal resolution of 1km within PWS and utilizes a recently-developed multi-scale 3DVAR data assimilation methodology along with freshwater runoff from land obtained via real-time execution of a digital elevation model. During the Sound Predictions Field Experiment (July 19-August 3, 2009) the system was run in real-time to support operations and incorporated all available real-time streams of data. Nowcasts were produced every 6h and a 48-h forecast was performed once a day. In addition, a sixteen-member ensemble of forecasts was executed on most days. All results were published at a web portal (http://ourocean.jpl.nasa.gov/PWS) in real time to support decision making.The performance of the system during Sound Predictions 2009 is evaluated. The ROMS results are first compared with the assimilated data as a consistency check. RMS differences of about 0.7°C were found between the ROMS temperatures and the observed vertical profiles of temperature that are assimilated. The ROMS salinities show greater discrepancies, tending to be too salty near the surface. The overall circulation patterns observed throughout the Sound are qualitatively reproduced, including the following evolution in time. During the first week of the experiment, the weather was quite stormy with strong southeasterly winds. This resulted in strong north to northwestward surface flow in much of the central PWS. Both the observed drifter trajectories and the ROMS nowcasts showed strong surface inflow into the Sound through the Hinchinbrook Entrance and strong generally northward to northwestward flow in the central Sound that was exiting through the Knight Island Passage and Montague Strait entrance. During the latter part of the second week when surface winds were light and southwesterly, the mean surface flow at the Hinchinbrook Entrance reversed to weak outflow and a cyclonic eddy formed in the central Sound. Overall, RMS differences between ROMS surface currents and observed HF radar surface currents in the central Sound were generally between 5 and 10cm/s, about 20-40% of the time mean current speeds.The ROMS reanalysis is then validated against independent observations. A comparison of the ROMS currents with observed vertical current profiles from moored ADCPs in the Hinchinbrook Entrance and Montague Strait shows good qualitative agreement and confirms the evolution of the near surface inflow/outflow at these locations described above. A comparison of the ROMS surface currents with drifter trajectories provided additional confirmation that the evolution of the surface flow described above was realistic. Forecasts of drifter locations had RMS errors of less than 10km for up to 36h. One and two-day forecasts of surface temperature, salinity and current fields were more skillful than persistence forecasts. In addition, ensemble mean forecasts were found to be slightly more skillful than single forecasts. Two case studies demonstrated the system's qualitative skill in predicting subsurface changes within the mixed layer measured by ships and autonomous underwater vehicles. In summary, the system is capable of producing a realistic evolution of the near-surface circulation within PWS including forecasts of up to two days of this evolution. Use of the products provided by the system during the experiment as part of the asset deployment decision making process demonstrated the value of accurate regional ocean forecasts in support of field experiments.

Assessing the spatial variability of mountain precipitation in California's Sierra Nevada using the Airborne Snow Observatory

NASA Astrophysics Data System (ADS)

Brandt, T.; Deems, J. S.; Painter, T. H.; Dozier, J.

2016-12-01

In California's Sierra Nevada, 10 or fewer winter storms are responsible for most of the annual precipitation, which falls mostly as snow. Presently, surface stations are used to measure the dynamics of mountain precipitation. However, even in places like the Sierra Nevada—one of the most gauged regions in the world—the paucity of surface stations can lead to large errors in precipitation thereby biasing both total water year and short-term streamflow forecasts. Remotely sensed snow depth and water equivalent, at a time scale that resolves storms, might provide a novel solution to the problems of: (1) quantifying the spatial variability of mountain precipitation; and (2) assessing gridded precipitation products that are mostly based on surface station interpolation. NASA's Airborne Snow Observatory (ASO), an imaging spectrometer and LiDAR system, has measured snow in the Tuolumne River Basin in California's Sierra Nevada for the past four years, 2013-2016; and, measurements will continue. Principally, ASO monitors the progression of melt for water supply forecasting, nonetheless, a number of flights bracketed storms allowing for estimates of snow accumulation. In this study we examine a few of the ASO recorded storms to determine both the basin and subbasin orographic effect as well as the spatial patterns in total precipitation. We then compare these results to a number of gridded climate products and weather models including: Daymet, the Parameter-elevation Regressions on Independent Slopes Model (PRISM), the North American Land Data Assimilation System (NLDAS-2), and the Weather Research and Forecasting (WRF) model. Finally, to put each ASO recorded storm into context, we use a climatology produced from snow pillows and the North American Regional Reanalysis (NARR) for 2014-2016 to examine key accumulation events, and classify storms based on their integrated water vapor flux.

Comparing Vertical Distributions of Water Vapor Flux within Two Landfalling Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Rutz, J. J.; Lavers, D. A.

2015-12-01

The West Coast of North America is frequently impacted by atmospheric rivers (ARs), regions of intense horizontal water vapor transport that often produce heavy rain, flooding, and landslides when they interact with near-coastal mountains. Recently, studies have shown that ARs penetrate farther inland on many occasions, with indications that the vertical distribution of vapor transport within the ARs may play a key role in this penetration (Alexander et al. 2015; Rutz et al. 2015). We hypothesize that the amount of near-coastal precipitation and the likelihood of AR penetration farther inland may be inversely linked by vertical distributions of vapor fluxes before, during, and after landfall. To explore whether differing vertical distributions of transport explain differing precipitation and penetration outcomes, we compare two landfalling ARs that had very similar spatial extents and rates of vertically integrated (total) vapor transport, but which nonetheless produced very different amounts of precipitation over northern California. The vertical distribution of water vapor flux, specific humidity, and wind speed during these two ARs are examined along several transects using cross-sectional analyses of the Climate Forecast System Reanalysis with a horizontal resolution of ~0.5° (~63 km) and a sigma-pressure hybrid coordinate at 64 vertical levels. In addition, we pursue similar analyses of forecasts from the NCEP Global Ensemble Forecast System GEFS to assess whether numerical weather prediction models accurately represent these distributions. Finally, we calculate backward trajectories from within each AR to examine whether or not the origins of their respective air parcels play a role in the resulting vertical distribution of water vapor flux. The results have major implications for two problems in weather prediction: (1) the near-coastal precipitation associated with landfalling ARs and (2) the likelihood of AR penetration farther inland.

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Seasonal forecasts for the agricultural sector in Peru through user-tailored indices

NASA Astrophysics Data System (ADS)

Sedlmeier, Katrin; Gubler, Stefanie; Spierig, Christoph; Quevedo, Karim; Escajadillo, Yury; Avalos, Griña; Liniger, Mark A.; Schwierz, Cornelia

2017-04-01

In the agricultural sector, the demand for seasonal forecast information is high since agriculture depends strongly on climatic conditions during the growing season. Unfavorable weather and climate events, such as droughts or frost events, can lead to crop losses and thereby to large economic damages or life-threatening conditions in case of subsistence farming. The generally used presentation form of tercile probabilities of seasonally averaged meteorological quantities are not specific enough for end users. More user-tailored seasonal information is necessary. For example, warmer than average temperatures might be favorable for a crop as long as they remain below a plant-specific critical threshold. If, on the other hand, too many days show temperatures above this critical threshold, a mitigation action such as e.g. changing the crop type would be required. In the framework of the CLIMANDES project (a pilot project of the Global Framework for Climate Services led by WMO [http://www.wmo.int/gfcs/climandes]), user-tailored seasonal forecast products are developed for the agricultural sector in the Peruvian Andes. Such products include indices such as e.g. the frost risk, the occurrence of long dry periods, or the start of the rainy season which is crucial to schedule sowing. Furthermore, more specific indices derived from crop requirement studies are elaborated such as the number of days exceeding or falling below plant specific temperature thresholds for given phenological stages. The applicability of these products highly depends on forecast skill. In this study, the potential predictability and the skill of selected indicators are presented using seasonal hindcast data of the ECMWF system 4 for Peru during the time period 1981-2010. Furthermore, the influence of ENSO on the prediction skill is investigated. In this study, reanalysis data, ground measurements, and a gridded precipitation dataset are used for verification. The results indicate that temperature-based indicators show sizeable skill in the Peruvian highlands while precipitation-based forecasts are much more challenging.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boer, George J.; Smith, Douglas M.; Cassou, Christophe

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Potential Seasonal Predictability for Winter Storms over Europe

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2017-04-01

Reliable seasonal forecasts of strong extra-tropical cyclones and windstorms would have great social and economical benefits, as these events are the most costly natural hazards over Europe. In a previous study we have shown good agreement of spatial climatological distributions of extra-tropical cyclones and wind storms in state-of-the-art multi-member seasonal prediction systems with reanalysis. We also found significant seasonal prediction skill of extra-tropical cyclones and windstorms affecting numerous European countries. We continue this research by investigating the mechanisms and precursor conditions (primarily over the North Atlantic) on a seasonal time scale leading to enhanced extra-tropical cyclone activity and winter storm frequency over Europe. Our results regarding mechanisms show that an increased surface temperature gradient at the western edge of the North Atlantic can be related to enhanced winter storm frequency further downstream causing for example a greater number of storms over the British Isles, as observed in winter 2013-14.The so-called "Horseshoe Index", a SST tripole anomaly pattern over the North Atlantic in the summer months can also cause a higher number of winter storms over Europe in the subsequent winter. We will show results of AMIP-type sensitivity experiments using an AGCM (ECHAM5), supporting this hypothesis. Finally we will analyse whether existing seasonal forecast systems are able to capture these identified mechanisms and precursor conditions affecting the models' seasonal prediction skill.

Evaluation of Probable Maximum Precipitation and Flood under Climate Change in the 21st Century

NASA Astrophysics Data System (ADS)

Gangrade, S.; Kao, S. C.; Rastogi, D.; Ashfaq, M.; Naz, B. S.; Kabela, E.; Anantharaj, V. G.; Singh, N.; Preston, B. L.; Mei, R.

2016-12-01

Critical infrastructures are potentially vulnerable to extreme hydro-climatic events. Under a warming environment, the magnitude and frequency of extreme precipitation and flood are likely to increase enhancing the needs to more accurately quantify the risks due to climate change. In this study, we utilized an integrated modeling framework that includes the Weather Research Forecasting (WRF) model and a high resolution distributed hydrology soil vegetation model (DHSVM) to simulate probable maximum precipitation (PMP) and flood (PMF) events over Alabama-Coosa-Tallapoosa River Basin. A total of 120 storms were selected to simulate moisture maximized PMP under different meteorological forcings, including historical storms driven by Climate Forecast System Reanalysis (CFSR) and baseline (1981-2010), near term future (2021-2050) and long term future (2071-2100) storms driven by Community Climate System Model version 4 (CCSM4) under Representative Concentrations Pathway 8.5 emission scenario. We also analyzed the sensitivity of PMF to various antecedent hydrologic conditions such as initial soil moisture conditions and tested different compulsive approaches. Overall, a statistical significant increase is projected for future PMP and PMF, mainly attributed to the increase of background air temperature. The ensemble of simulated PMP and PMF along with their sensitivity allows us to better quantify the potential risks associated with hydro-climatic extreme events on critical energy-water infrastructures such as major hydropower dams and nuclear power plants.

Toward the assimilation of biogeochemical data in the CMEMS BIOMER coupled physical-biogeochemical operational system

NASA Astrophysics Data System (ADS)

Lamouroux, Julien; Testut, Charles-Emmanuel; Lellouche, Jean-Michel; Perruche, Coralie; Paul, Julien

2017-04-01

The operational production of data-assimilated biogeochemical state of the ocean is one of the challenging core projects of the Copernicus Marine Environment Monitoring Service. In that framework - and with the April 2018 CMEMS V4 release as a target - Mercator Ocean is in charge of improving the realism of its global ¼° BIOMER coupled physical-biogeochemical (NEMO/PISCES) simulations, analyses and re-analyses, and to develop an effective capacity to routinely estimate the biogeochemical state of the ocean, through the implementation of biogeochemical data assimilation. Primary objectives are to enhance the time representation of the seasonal cycle in the real time and reanalysis systems, and to provide a better control of the production in the equatorial regions. The assimilation of BGC data will rely on a simplified version of the SEEK filter, where the error statistics do not evolve with the model dynamics. The associated forecast error covariances are based on the statistics of a collection of 3D ocean state anomalies. The anomalies are computed from a multi-year numerical experiment (free run without assimilation) with respect to a running mean in order to estimate the 7-day scale error on the ocean state at a given period of the year. These forecast error covariances rely thus on a fixed-basis seasonally variable ensemble of anomalies. This methodology, which is currently implemented in the "blue" component of the CMEMS operational forecast system, is now under adaptation to be applied to the biogeochemical part of the operational system. Regarding observations - and as a first step - the system shall rely on the CMEMS GlobColour Global Ocean surface chlorophyll concentration products, delivered in NRT. The objective of this poster is to provide a detailed overview of the implementation of the aforementioned data assimilation methodology in the CMEMS BIOMER forecasting system. Focus shall be put on (1) the assessment of the capabilities of this data assimilation methodology to provide satisfying statistics of the model variability errors (through space-time analysis of dedicated representers of satellite surface Chla observations), (2) the dedicated features of the data assimilation configuration that have been implemented so far (e.g. log-transformation of the analysis state, multivariate Chlorophyll-Nutrient control vector, etc.) and (3) the assessment of the performances of this future operational data assimilation configuration.

Wind-Stress Simulations and Equatorial Dynamics in an AGCM. Part 1; Basic Results from a 1979-1999 Forced SST Experiment

NASA Technical Reports Server (NTRS)

Bacmeister, Julio T.; Suarez, Max J.; Einaudi, Franco (Technical Monitor)

2001-01-01

This is the first of a two part study examining the connection of the equatorial momentum budget in an AGCM (Atmospheric General Circulation Model), with simulated equatorial surface wind stresses over the Pacific. The AGCM used in this study forms part of a newly developed coupled forecasting system used at NASA's Seasonal- to-Interannual Prediction Project. Here we describe the model and present results from a 20-year (1979-1999) AMIP-type experiment forced with observed SSTs (Sea Surface Temperatures). Model results are compared them with available observational data sets. The climatological pattern of extra-tropical planetary waves as well as their ENSO-related variability is found to agree quite well with re-analysis estimates. The model's surface wind stress is examined in detail, and reveals a reasonable overall simulation of seasonal interannual variability, as well as seasonal mean distributions. However, an excessive annual oscillation in wind stress over the equatorial central Pacific is found. We examine the model's divergent circulation over the tropical Pacific and compare it with estimates based on re-analysis data. These comparisons are generally good, but reveal excessive upper-level convergence in the central Pacific. In Part II of this study a direct examination of individual terms in the AGCM's momentum budget is presented. We relate the results of this analysis to the model's simulation of surface wind stress.

Assessing various approaches for flash flood forecasting in the Yzeron periurban catchment (150 km2) south-east Lyon, France

NASA Astrophysics Data System (ADS)

Braud, Isabelle; Breil, Pascal; Javelle, Pierre; Pejakovic, Nikola; Guérin, Stéphane

2017-04-01

The Yzeron periurban catchment (150 km2) is prone to flash floods leading to overflow in the downstream part of the catchment. A prevention and management plan has been approved and the set-up of a flood forecasting system is planned. The present study presents a comparison of several solutions for flood forecasting in the catchment. It is based on an extensive data collection (rain gauges, radar/rain gauge reanalyses, discharge and water level data) from this experimental catchment. A set of rainfall-runoff events leading to floods (problematic and non-problematic floods) was extracted and formed the basis for the definition of a first forecasting method. It is based on data analysis and the identification of explaining factors amongst the following: rainfall amount, intensity, antecedent rainfall, initial discharge. Several statistical methods including Factorial Analysis of Mixed Data and Classification and Regression Tree were used for this purpose. They showed that several classes of problematic floods can be identified. The first one is related to wet conditions characterized with high initial discharge and antecedent rainfall. The second class is driven by rainfall amount, initial discharge and rainfall intensity. Thresholds of these variables can be identified to provide a first warning. The second forecasting method assessed in the study is the system that will be operational in France in 2017, based on the AIGA method (Javelle et al., 2016). For this purpose, 18-year discharge simulation using the hydrological model of the AIGA method, forced using radar/rain gauges reanalysis were available at 44 locations within the catchment. The dates for which quantiles of a given return period were overtopped were identified and compared with the list of problematic events. The AIGA method was found relevant in identifying the most problematic events, but the lead time needs further investigation in order to assess the usefulness for population warning. References: Pierre Javelle, Didier Organde, Julie Demargne, Clotilde Saint-Martin, Céline de Saint-Aubin, Léa Garandeau and Bruno Janet (2016). Setting up a French national flash flood warning system for ungauged catchments based on the AIGA method. E3S Web of Conferences 7, 18010 (2016), 3rd European Conference on Flood Risk Management (FLOODrisk 2016), http://dx.doi.org/10.1051/e3sconf/20160718010

Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

NASA Astrophysics Data System (ADS)

Jeong, Sang-Hun; Kim, Jinah; Heo, Ki-Young; Park, Kwang-Soon

2017-04-01

In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages. In Korea, an operational oceanographic system (KOOS) has been developed by the Korea institute of ocean science and technology (KIOST) and KOOS provides daily basis 72-hours' ocean forecasts such as wind, water elevation, sea currents, water temperature, salinity, and waves which are computed from not only meteorological and hydrodynamic model (WRF, ROMS, MOM, and MOHID) but also wave models (WW-III and SWAN). In order to evaluate the model performance and guarantee a certain level of accuracy of ocean forecasts, a Skill Assessment (SA) system was established as a one of module in KOOS. It has been performed through comparison of model results with in-situ observation data and model errors have been quantified with skill scores. Statistics which are used in skill assessment are including a measure of both errors and correlations such as root-mean-square-error (RMSE), root-mean-square-error percentage (RMSE%), mean bias (MB), correlation coefficient (R), scatter index (SI), circular correlation (CC) and central frequency (CF) that is a frequency with which errors lie within acceptable error criteria. It should be utilized simultaneously not only to quantify an error but also to improve an accuracy of forecasts by providing a feedback interactively. However, in an abnormal phenomena such as high-height swell-like waves in the East coast of Korea, it requires more advanced and optimized error quantification method that allows to predict the abnormal waves well and to improve the accuracy of forecasts by supporting modification of physics and numeric on numerical models through sensitivity test. In this study, we proposed an appropriate method of error quantification especially on abnormal high waves which are occurred by local weather condition. Furthermore, we introduced that how the quantification errors are contributed to improve wind-wave modeling by applying data assimilation and utilizing reanalysis data.

Global climatology of planetary boundary layer top obtained from multi-satellite GPS RO observations

NASA Astrophysics Data System (ADS)

Basha, Ghouse; Kishore, P.; Ratnam, M. Venkat; Ravindra Babu, S.; Velicogna, Isabella; Jiang, Jonathan H.; Ao, Chi O.

2018-05-01

Accurate estimation of the planetary boundary layer (PBL) top is essential for air quality prediction, weather forecast, and assessment of regional and global climate models. In this article, the long-term climatology of seasonal, global distribution of PBL is presented by using global positioning system radio occultation (GPSRO) based payloads such as Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), Communication/Navigation Outage Forecast System (C/NOFS), TerraSAR-X, and The Gravity Recovery and Climate Experiment (GRACE) from the year 2006-2015. We used Wavelet Covariance Transform (WCT) technique for precise PBL top identification. The derived PBL top from GPSRO data is rigorously evaluated with GPS radiosonde data over Gadanki. Significant seasonal variation is noticed in both radiosonde and GPSRO observations. Further, we compared the PBL obtained GPS RO with global radiosonde network and observed very good correlation. The number of occultations reaching down to 500 m and retrieval rate of PBL top from WCT method is very high in mid-latitudes compared to tropical latitudes. The global distribution of PBL top shows significant seasonal variation with higher during summer followed by spring, fall, and minimum in winter. In the vicinity of Inter Tropical Convergence Zone (ITCZ), the PBL top is high over eastern Pacific compared to other regions. The ERA-Interim reanalysis data underestimate the PBL top compared to GPS RO observations due to different measurement techniques. The seasonal variation of global averaged PBL top over land and ocean shows contrasting features at different latitude bands.

On the improvement of wave and storm surge hindcasts by downscaled atmospheric forcing: application to historical storms

NASA Astrophysics Data System (ADS)

Bresson, Émilie; Arbogast, Philippe; Aouf, Lotfi; Paradis, Denis; Kortcheva, Anna; Bogatchev, Andrey; Galabov, Vasko; Dimitrova, Marieta; Morvan, Guillaume; Ohl, Patrick; Tsenova, Boryana; Rabier, Florence

2018-04-01

Winds, waves and storm surges can inflict severe damage in coastal areas. In order to improve preparedness for such events, a better understanding of storm-induced coastal flooding episodes is necessary. To this end, this paper highlights the use of atmospheric downscaling techniques in order to improve wave and storm surge hindcasts. The downscaling techniques used here are based on existing European Centre for Medium-Range Weather Forecasts reanalyses (ERA-20C, ERA-40 and ERA-Interim). The results show that the 10 km resolution data forcing provided by a downscaled atmospheric model gives a better wave and surge hindcast compared to using data directly from the reanalysis. Furthermore, the analysis of the most extreme mid-latitude cyclones indicates that a four-dimensional blending approach improves the whole process, as it assimilates more small-scale processes in the initial conditions. Our approach has been successfully applied to ERA-20C (the 20th century reanalysis).

Exploring predictive performance: A reanalysis of the geospace model transition challenge

NASA Astrophysics Data System (ADS)

Welling, D. T.; Anderson, B. J.; Crowley, G.; Pulkkinen, A. A.; Rastätter, L.

2017-01-01

The Pulkkinen et al. (2013) study evaluated the ability of five different geospace models to predict surface dB/dt as a function of upstream solar drivers. This was an important step in the assessment of research models for predicting and ultimately preventing the damaging effects of geomagnetically induced currents. Many questions remain concerning the capabilities of these models. This study presents a reanalysis of the Pulkkinen et al. (2013) results in an attempt to better understand the models' performance. The range of validity of the models is determined by examining the conditions corresponding to the empirical input data. It is found that the empirical conductance models on which global magnetohydrodynamic models rely are frequently used outside the limits of their input data. The prediction error for the models is sorted as a function of solar driving and geomagnetic activity. It is found that all models show a bias toward underprediction, especially during active times. These results have implications for future research aimed at improving operational forecast models.

Quantifying How Observations Inform a Numerical Reanalysis of Hawaii

NASA Astrophysics Data System (ADS)

Powell, B. S.

2017-11-01

When assimilating observations into a model via state-estimation, it is possible to quantify how each observation changes the modeled estimate of a chosen oceanic metric. Using an existing 2 year reanalysis of Hawaii that includes more than 31 million observations from satellites, ships, SeaGliders, and autonomous floats, I assess which observations most improve the estimates of the transport and eddy kinetic energy. When the SeaGliders were in the water, they comprised less than 2.5% of the data, but accounted for 23% of the transport adjustment. Because the model physics constrains advanced state-estimation, the prescribed covariances are propagated in time to identify observation-model covariance. I find that observations that constrain the isopycnal tilt across the transport section provide the greatest impact in the analysis. In the case of eddy kinetic energy, observations that constrain the surface-driven upper ocean have more impact. This information can help to identify optimal sampling strategies to improve both state-estimates and forecasts.

Dynamo-based scheme for forecasting the magnitude of solar activity cycles

NASA Technical Reports Server (NTRS)

Layden, A. C.; Fox, P. A.; Howard, J. M.; Sarajedini, A.; Schatten, K. H.

1991-01-01

This paper presents a general framework for forecasting the smoothed maximum level of solar activity in a given cycle, based on a simple understanding of the solar dynamo. This type of forecasting requires knowledge of the sun's polar magnetic field strength at the preceding activity minimum. Because direct measurements of this quantity are difficult to obtain, the quality of a number of proxy indicators already used by other authors is evaluated, which are physically related to the sun's polar field. These indicators are subjected to a rigorous statistical analysis, and the analysis technique for each indicator is specified in detail in order to simplify and systematize reanalysis for future use. It is found that several of these proxies are in fact poorly correlated or uncorrelated with solar activity, and thus are of little value for predicting activity maxima. Also presented is a scheme in which the predictions of the individual proxies are combined via an appropriately weighted mean to produce a compound prediction. The scheme is then applied to the current cycle 22, and a maximum smoothed international sunspot number of 171 + or - 26 is estimated.

Analysis of extreme summers and prior late winter/spring conditions in central Europe

NASA Astrophysics Data System (ADS)

Träger-Chatterjee, C.; Müller, R. W.; Bendix, J.

2013-05-01

Drought and heat waves during summer in mid-latitudes are a serious threat to human health and agriculture and have negative impacts on the infrastructure, such as problems in energy supply. The appearance of such extreme events is expected to increase with the progress of global warming. A better understanding of the development of extremely hot and dry summers and the identification of possible precursors could help improve existing seasonal forecasts in this regard, and could possibly lead to the development of early warning methods. The development of extremely hot and dry summer seasons in central Europe is attributed to a combined effect of the dominance of anticyclonic weather regimes and soil moisture-atmosphere interactions. The atmospheric circulation largely determines the amount of solar irradiation and the amount of precipitation in an area. These two variables are themselves major factors controlling the soil moisture. Thus, solar irradiation and precipitation are used as proxies to analyse extreme sunny and dry late winter/spring and summer seasons for the period 1958-2011 in Germany and adjacent areas. For this purpose, solar irradiation data from the European Center for Medium Range Weather Forecast 40-yr and interim re-analysis dataset, as well as remote sensing data are used. Precipitation data are taken from the Global Precipitation Climatology Project. To analyse the atmospheric circulation geopotential data at 850 hPa are also taken from the European Center for Medium Range Weather Forecast 40-yr and interim re-analysis datasets. For the years in which extreme summers in terms of high solar irradiation and low precipitation are identified, the previous late winter/spring conditions of solar irradiation and precipitation in Germany and adjacent areas are analysed. Results show that if the El Niño-Southern Oscillation (ENSO) is not very intensely developed, extremely high solar irradiation amounts, together with extremely low precipitation amounts during late winter/spring, might serve as precursor of extremely sunny and dry summer months to be expected.

A virtual remote sensing observation network for continuous, near-real-time monitoring of atmospheric instability

NASA Astrophysics Data System (ADS)

Toporov, Maria; Löhnert, Ulrich; Potthast, Roland; Cimini, Domenico; De Angelis, Francesco

2017-04-01

Short-term forecasts of current high-resolution numerical weather prediction models still have large deficits in forecasting the exact temporal and spatial location of severe, locally influenced weather such as summer-time convective storms or cool season lifted stratus or ground fog. Often, the thermodynamic instability - especially in the boundary layer - plays an essential role in the evolution of weather events. While the thermodynamic state of the atmosphere is well measured close to the surface (i.e. 2 m) by in-situ sensors and in the upper troposphere by satellite sounders, the planetary boundary layer remains a largely under-sampled region of the atmosphere where only sporadic information from radiosondes or aircraft observations is available. The major objective of the presented DWD-funded project ARON (Extramural Research Programme) is to overcome this observational gap and to design an optimized network of ground based microwave radiometers (MWR) and compact Differential Absorption Lidars (DIAL) for a continuous, near-real-time monitoring of temperature and humidity in the atmospheric boundary layer in order to monitor thermodynamic (in)stability. Previous studies showed, that microwave profilers are well suited for continuously monitoring the temporal development of atmospheric stability (i.e. Cimini et al., 2015) before the initiation of deep convection, especially in the atmospheric boundary layer. However, the vertical resolution of microwave temperature profiles is best in the lowest kilometer above the surface, decreasing rapidly with increasing height. In addition, humidity profile retrievals typically cannot be resolved with more than two degrees of freedom for signal, resulting in a rather poor vertical resolution throughout the troposphere. Typical stability indices used to assess the potential of convection rely on temperature and humidity values not only in the region of the boundary layer but also in the layers above. Therefore, satellite remote sensing (i.e. SEVIRI, AMSU) is used to complement observations from a virtual ground-based microwave radiometer network based on the reanalysis of the COSMO model for Europe. In this contribution, we present a synergetic retrieval algorithm of stability indices from satellite observations and ground-based microwave measurements based on the COSMO-DE reanalysis as truth. In order to make the approach feasible for data assimilation applications at national weather services, we simulate satellite observations with the standard RTTOV model and use the newly developed RTTOV-gb (ground-based) for the ground-based radiometers (De Angelis et al., 2016). For the detection of significant instabilities, we show the synergy benefit in terms of uncertainty reduction, probability of detection and other forecast skill scores. The overall goal of ARON is to quantify the impact of ground-based vertical profilers within an integrated forecasting system, which combines short-term and now-casting.

The joint methane profiles retrieval approach from GOSAT TIR and SWIR spectra

NASA Astrophysics Data System (ADS)

Zadvornykh, Ilya V.; Gribanov, Konstantin G.; Zakharov, Vyacheslav I.; Imasu, Ryoichi

2017-11-01

In this paper we present a method, using methane as example, which allows more accurate greenhouse gases retrieval in the Earth's atmosphere. Using the new version of the FIRE-ARMS software, supplemented with the VLIDORT vector radiation transfer model, we carried out joint methane retrieval from TIR (Thermal Infrared Range) and SWIR (ShortWavelength Infrared Range) GOSAT spectra using optimal estimation method. MACC reanalysis data from the European Center for Medium-Range Forecasts (ECMWF), supplemented by data from aircraft measurements of the HIPPO experiment were used as a statistical ensemble.

Relation between Ocean SST Dipoles and Downwind Continental Croplands Assessed for Early Management Using Satellite-based Photosynthesis Models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2015-04-01

Crop-monitoring systems with the unit of carbon-dioxide sequestration for environmental issues related to climate adaptation to global warming have been improved using satellite-based photosynthesis and meteorological conditions. Early management of crop status is desirable for grain production, stockbreeding, and bio-energy providing that the seasonal climate forecasting is sufficiently accurate. Incorrect seasonal forecasting of crop production can damage global social activities if the recognized conditions are unsatisfied. One cause of poor forecasting related to the atmospheric dynamics at the Earth surface, which reflect the energy budget through land surface, especially the oceans and atmosphere. Recognition of the relation between SST anomalies (e.g. ENSO, Atlantic Niño, Indian dipoles, and Ningaloo Niño) and crop production, as expressed precisely by photosynthesis or the sequestrated-carbon rate, is necessary to elucidate the mechanisms related to poor production. Solar radiation, surface air temperature, and water stress all directly affect grain vegetation photosynthesis. All affect stomata opening, which is related to the water balance or definition by the ratio of the Penman potential evaporation and actual transpiration. Regarding stomata, present data and reanalysis data give overestimated values of stomata opening because they are extended from wet models in forests rather than semi-arid regions commonly associated with wheat, maize, and soybean. This study applies a complementary model based on energy conservation for semi-arid zones instead of the conventional Penman-Monteith method. Partitioning of the integrated Net PSN enables precise estimation of crop yields by modifying the semi-closed stomata opening. Partitioning predicts production more accurately using the cropland distribution already classified using satellite data. Seasonal crop forecasting should include near-real-time monitoring using satellite-based process crop models to avoid social difficulties that can derive from uncertain seasonal predictions produced from long-term forecasting. Acknowledgement The author appreciates scientific discussions held with the application team of seasonal prediction at the Japan Agency for Marine-Earth Science and Technology. Key words: crop production, monitoring, forecasting, SST anomaly, remote sensing

Identification and climatology of cut-off lows near the tropopause.

PubMed

Nieto, R; Sprenger, M; Wernli, H; Trigo, R M; Gimeno, L

2008-12-01

Cut-off low pressure systems (COLs) are defined as closed lows in the upper troposphere that have become completely detached from the main westerly current. These slow-moving systems often affect the weather conditions at the earth's surface and also work as a mechanism of mass transfer between the stratosphere and the troposphere, playing a significant role in the net flow of tropospheric ozone. In the first part of this work we provide a comprehensive summary of results obtained in previous studies of COLs. Following this, we present three long-term climatologies of COLs. The first two climatologies are based on the conceptual model of a COL, using European Centre for Medium-range Weather Forecasts (ECMWF) analyses (1958-2002) and National Centers for Environmental Prediction-National Center for Atmospheric Research (1948-2006) reanalysis data sets. The third climatology uses a different method of detection, which is based on using potential vorticity as the physical parameter of diagnosis. This approach was applied only to the ECMWF reanalysis data. The final part of the paper is devoted to comparing results obtained by these different climatologies in terms of areas of preferential occurrence, life span, and seasonal cycle. Despite some key differences, the three climatologies agree in terms of the main areas of COL occurrence, namely (1) southwestern Europe, (2) the eastern north Pacific coast, and (3) the north China-Siberian region. However, it is also shown that the detection of these areas of main COL occurrence, as obtained using the potential vorticity approach, depends on the level of isentropic analysis used.

The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part 1: African Wave Circulation

NASA Technical Reports Server (NTRS)

Noble, Erik; Druyan, Leonard M.; Fulakeza, Matthew

2014-01-01

The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.

NVAP

Atmospheric Science Data Center

2013-09-05

... of creating stable, community accepted Earth System Data Records (ESDRs) for a variety of geophysical time series. A reanalysis and ... of creating stable, community accepted Earth System Data Records (ESDRs) for a variety of geophysical time series. A reanalysis and ...

Wind and wave extremes over the world oceans from very large ensembles

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan; Abdalla, Saleh; Bidlot, Jean-Raymond; Janssen, Peter A. E. M.

2014-07-01

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240 h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the data set. The period (9 years) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for nonparametric 100 year return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.

Near-Surface Meteorology During the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of Reanalyses and Global Climate Models.

NASA Technical Reports Server (NTRS)

De Boer, G.; Shupe, M.D.; Caldwell, P.M.; Bauer, Susanne E.; Persson, O.; Boyle, J.S.; Kelley, M.; Klein, S.A.; Tjernstrom, M.

2014-01-01

Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

Application Of Multi-grid Method On China Seas' Temperature Forecast

NASA Astrophysics Data System (ADS)

Li, W.; Xie, Y.; He, Z.; Liu, K.; Han, G.; Ma, J.; Li, D.

2006-12-01

Correlation scales have been used in traditional scheme of 3-dimensional variational (3D-Var) data assimilation to estimate the background error covariance for the numerical forecast and reanalysis of atmosphere and ocean for decades. However there are still some drawbacks of this scheme. First, the correlation scales are difficult to be determined accurately. Second, the positive definition of the first-guess error covariance matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. (2005) indicated that a traditional 3D-Var only corrects some certain wavelength errors and its accuracy depends on the accuracy of the first-guess covariance. And in general, short wavelength error can not be well corrected until long one is corrected and then inaccurate first-guess covariance may mistakenly take long wave error as short wave ones and result in erroneous analysis. For the purpose of quickly minimizing the errors of long and short waves successively, a new 3D-Var data assimilation scheme, called multi-grid data assimilation scheme, is proposed in this paper. By assimilating the shipboard SST and temperature profiles data into a numerical model of China Seas, we applied this scheme in two-month data assimilation and forecast experiment which ended in a favorable result. Comparing with the traditional scheme of 3D-Var, the new scheme has higher forecast accuracy and a lower forecast Root-Mean-Square (RMS) error. Furthermore, this scheme was applied to assimilate the SST of shipboard, AVHRR Pathfinder Version 5.0 SST and temperature profiles at the same time, and a ten-month forecast experiment on sea temperature of China Seas was carried out, in which a successful forecast result was obtained. Particularly, the new scheme is demonstrated a great numerical efficiency in these analyses.

LANL* V1.0: a radiation belt drift shell model suitable for real-time and reanalysis applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koller, Josep; Reeves, Geoffrey D; Friedel, Reiner H W

2008-01-01

Space weather modeling, forecasts, and predictions, especially for the radiation belts in the inner magnetosphere, require detailed information about the Earth's magnetic field. Results depend on the magnetic field model and the L* (pron. L-star) values which are used to describe particle drift shells. Space wather models require integrating particle motions along trajectories that encircle the Earth. Numerical integration typically takes on the order of 10{sup 5} calls to a magnetic field model which makes the L* calculations very slow, in particular when using a dynamic and more accurate magnetic field model. Researchers currently tend to pick simplistic models overmore » more accurate ones but also risking large inaccuracies and even wrong conclusions. For example, magnetic field models affect the calculation of electron phase space density by applying adiabatic invariants including the drift shell value L*. We present here a new method using a surrogate model based on a neural network technique to replace the time consuming L* calculations made with modern magnetic field models. The advantage of surrogate models (or meta-models) is that they can compute the same output in a fraction of the time while adding only a marginal error. Our drift shell model LANL* (Los Alamos National Lab L-star) is based on L* calculation using the TSK03 model. The surrogate model has currently been tested and validated only for geosynchronous regions but the method is generally applicable to any satellite orbit. Computations with the new model are several million times faster compared to the standard integration method while adding less than 1% error. Currently, real-time applications for forecasting and even nowcasting inner magnetospheric space weather is limited partly due to the long computing time of accurate L* values. Without them, real-time applications are limited in accuracy. Reanalysis application of past conditions in the inner magnetosphere are used to understand physical processes and their effect. Without sufficiently accurate L* values, the interpretation of reanalysis results becomes difficult and uncertain. However, with a method that can calculate accurate L* values orders of magnitude faster, analyzing whole solar cycles worth of data suddenly becomes feasible.« less

Sensitivity to Madden-Julian Oscillation variations on heavy precipitation over the contiguous United States

NASA Astrophysics Data System (ADS)

Jones, Charles; Carvalho, Leila M. V.

2014-10-01

The Madden-Julian Oscillation (MJO) is the most prominent mode of tropical intraseasonal variability in the climate system and has worldwide influences on the occurrences and forecasts of heavy precipitation. This paper investigates the sensitivity of precipitation over the contiguous United States (CONUS) in a case study (boreal 2004-05 winter). Several major storms affected the western and eastern CONUS producing substantial economic and social impacts including loss of lives. The Weather Research and Forecasting (WRF) model is used to perform experiments to test the significance of the MJO amplitude. The control simulation uses the MJO amplitude observed by reanalysis, whereas the amplitude is modified in perturbation experiments. WRF realistically simulates the precipitation variability over the CONUS, although large biases occur over the Western and Midwest United States. Daily precipitation is aggregated in western, central and eastern sectors and the frequency distribution is analyzed. Increases in MJO amplitude produce moderate increases in the median and interquartile range and large and robust increases in extreme (90th and 95th percentiles) precipitation. The MJO amplitude clearly affects the transport of moisture from the tropical Pacific and Gulf of Mexico into North America providing moist rich air masses and the dynamical forcing that contributes to heavy precipitation.

A First Look at Surface Meteorology in the Arctic System Reanalysis

NASA Astrophysics Data System (ADS)

Slater, A. G.; Serreze, M. C.; Asr-Team, A.

2010-12-01

The Arctic System Reanalysis (ASR) is a joint venture between several universities (Ohio-State Uni., Uni. Colorado, Uni. Illinois UC, Uni. Alaska) and NCAR. It is a regional reanalysis that will span the period 2000-2010, possibly continuing into the future. Compared to current regional or global reanalyses it will have a spatial resolution twice that of prior efforts; a final product is expected to be an equal area projection of 15km grid boxes. The domain encompasses all the Arctic Ocean drainage areas. Several new reanalysis applications have been implemented, with some being Arctic specific - for example satellite derived sea ice age is translated into thickness and MODIS surface albedo is to be ingested. A preliminary ASR run has been performed for the period June 2007 - December 2008 at a reduced resolution of 30km. Here we make a comparison of all recent reanalysis products (NARR, MERRA, ERA-I, CFSRR) to both the ASR and observations at 350 surface stations in the Western Arctic; there is a major focus on Alaska. An intercomparison of surface variables (which are perhaps the most used reanalysis data) has been undertaken including temperature, humidity and solar radiation. Results indicate that the level of discrepancy between reanalysis data and observations is of similar magnitude as it is between all the reanalysis products; possibly suggesting that we have reached the limit of repersentativeness when comparing grid boxes to point measurements.

Dynamic downscaling over western Himalayas: Impact of cloud microphysics schemes

NASA Astrophysics Data System (ADS)

Tiwari, Sarita; Kar, Sarat C.; Bhatla, R.

2018-03-01

Due to lack of observation data in the region of inhomogeneous terrain of the Himalayas, detailed climate of Himalayas is still unknown. Global reanalysis data are too coarse to represent the hydroclimate over the region with sharp orography gradient in the western Himalayas. In the present study, dynamic downscaling of the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis-Interim (ERA-I) dataset over the western Himalayas using high-resolution Weather Research and Forecast (WRF) model has been carried out. Sensitivity studies have also been carried out using convection and microphysics parameterization schemes. The WRF model simulations have been compared against ERA-I and available station observations. Analysis of the results suggests that the WRF model has simulated the hydroclimate of the region well. It is found that in the simulations that the impact of convection scheme is more during summer months than in winter. Examination of simulated results using various microphysics schemes reveal that the WRF single-moment class-6 (WSM6) scheme simulates more precipitation on the upwind region of the high mountain than that in the Morrison and Thompson schemes during the winter period. Vertical distribution of various hydrometeors shows that there are large differences in mixing ratios of ice, snow and graupel in the simulations with different microphysics schemes. The ice mixing ratio in Morrison scheme is more than WSM6 above 400 hPa. The Thompson scheme favors formation of more snow than WSM6 or Morrison schemes while the Morrison scheme has more graupel formation than other schemes.

Winter wheat production forecast in United States of America using AVHRR historical data and NCAR Growing Degree Day

NASA Astrophysics Data System (ADS)

Claverie, M.; Franch, B.; Vermote, E.; Becker-Reshef, I.; Justice, C. O.

2015-12-01

Wheat is one of the key cereals crop grown worldwide. Thus, accurate and timely forecasts of its production are critical for informing agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) used an empirical generalized model for forecasting winter wheat production using combined BRDF-corrected daily surface reflectance from the Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel, and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. Recently, Franch et al. (2015) included Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts between a month to a month and a half prior to the peak NDVI (i.e. 1-2.5 months prior to harvest), while conserving the accuracy of the original model. In this study, we apply these methods to historical data from the Advanced Very High Resolution Radiometer (AVHRR). We apply both the original and the modified model to United States of America from 1990 to 2014 and inter-compare the AVHRR results to MODIS from 2000 to 2014.

Data Analysis of GPM Constellation Satellites-IMERG and ERA-Interim precipitation products over West of Iran

NASA Astrophysics Data System (ADS)

Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

2016-04-01

Precipitation is a critical component of the Earth's hydrological cycle. The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Especially in data sparse regions with insufficient radar coverage, satellite information can provide a spatial and temporal context. Nonetheless, evaluation of satellite precipitation is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation. Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards. In situ observations over mountainous areas are mostly limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. One of the newest and blended methods that use multi-satellites and multi-sensors has been developed for estimating global precipitation. The considered data set known as Integrated Multi-satellitE Retrievals (IMERG) for GPM (Global Precipitation Measurement) is routinely produced by the GPM constellation satellites. Moreover, recent efforts have been put into the improvement of the precipitation products derived from reanalysis systems, which has led to significant progress. One of the best and a worldwide used model is developed by the European Centre for Medium Range Weather Forecasts (ECMWF). They have produced global reanalysis daily precipitation, known as ERA-Interim. This study has evaluated one year of precipitation data from the GPM-IMERG and ERA-Interim reanalysis daily time series over West of Iran. IMERG and ERA-Interim yield underestimate the observed values while IMERG underestimated slightly and performed better when precipitation is greater than 10mm. Furthermore, with respect to evaluation of probability of detection (POD), threat score (TS), false alarm ratio (FAR) and probability of false detection (POFD) IMERG yields a better value of POD, TS, FAR and POFD in comparison to era-Interim. Overall, ERA-Interim product produced fewer robust results when compared to IMERG.

The Representation of Extra-tropical Cyclones in Recent Re-Analyses: ERA-Interim, NASA-MERRA, NCEP-CFS and JRA25

NASA Astrophysics Data System (ADS)

Hodges, K.

2010-12-01

Re-analyses are produced using a forecast model, data assimilation system and historical observations. Whilst the observations are common between the re-analyses the way they are assimilated and the forecast model used are often different between the re-analyses which can introduce uncertainty in the representation of particular phenomena between the re-analyses, for example the distribution and properties of weather systems. It is important to inter-compare re-analyses to determine the uncertainty in their representation of the atmosphere, its circulation and weather systems in order to have confidence in their use for studies of the atmosphere and validating climate models. The four recent re-analyses, ERA-Interim, NASA-MERRA, NCEP-CFS and JRA25 are explored and compared for the representation of synoptic scale extra-tropical cyclones. Previous studies of the older re-analyses. ERA40, NCEP-NCAR and DOE has shown that whilst in the NH there was relatively good agreement between the re-analyses in terms of the distribution and properties of extra-tropical cyclones, in the SH there was much larger uncertainty. The newest re-analyses are produced at much higher resolutions than previous re-analyses, in addition more modern data assimilation systems and forecast models have been used. Hence, it would be hoped that the representation of cyclones will be improved to the same extent as that seen in modern NWP systems. This study contrasts extra-tropical cyclones, their distribution and properties, between these new re-analyses and compares them with cyclones in the slightly older though lower resolution JRA25 re-analysis. Results will show that in general in the higher resolution re-analysis more cyclones are identified than in JRA25. In the NH the distribution of storms agrees as well if not better than was the case for the older re-analyses. However, it is in the SH that the largest improvement in agreement is seen for the distribution of storms. For ERA-Interim, NASA-MERRA and NCEP-CFS the agreement in the SH is almost as good as in the NH with the best agreement occurring between ERA-Interim and NCEP-CFS. However, the comparison with JRA25 shows the same level of uncertainty as seen with the older re-analyses. Determining the separation distances of storms using storm matching confirm these results. The biggest differences between the re-analyses occurs for the intensity of storms with the NASA-MERRA having consistently the strongest extreme storms in terms of pressure and winds and JRA25 the weakest, ERA-Interim and NCEP-CFS are very similar in this respect. Using vorticity as an intensity measure shows the greatest sensitivity and goes with resolution. If time permits a comparison of the structure of the storms will also be presented. The approach used only highlights the uncertainty between the re-analyses it does not say which one is right. To try to address this some early results of comparing the re-analyses directly with observations of low level winds from scatterometers in the vicinity of storms will be presented if time permits.

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

NASA Astrophysics Data System (ADS)

Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang

2017-08-01

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Extension of the SIM Hydrometeorological Reanalysis Over the Entire 20th Century by Combination of Observations and Statistical Downscaling

NASA Astrophysics Data System (ADS)

Minvielle, M.; Céron, J.; Page, C.

2013-12-01

The SAFRAN-ISBA-MODCOU (SIM) system is a combination of three different components: an atmospheric analysis system (SAFRAN) providing the atmospheric forcing for a land surface model (ISBA) that computes surface water and energy budgets and a hydrological model (MODCOU) that provides river flows and level of several aquifers. The variables generated by the SIM chain constitute the SIM reanalysis and the current version only covers the 1958-2012 period. However, long climate datasets are required for evaluation and verification of climate hindcasts/forecasts and to isolate the contribution of natural decadal variability from that of anthropogenic forcing to climate variations. The aim of this work is to extend of the fine-mesh SIM reanalysis to the entire 20th century, especially focusing on temperature and rainfall over France, but also soil wetness and river flows. This extension will first allow a detailed investigation of the influence of decadal variability on France at very fine spatial scales and will provide crucial information for climate model evaluation. Before 1958, the density of available observations from Météo-France necessary to force SAFRAN (rainfall, snow, wind, temperature, humidity, cloudiness) is much lower than today, and not sufficient to produce a correct SIM reanalysis. That's why is has been decided to use the available atmospheric observations over the past decades combined to a statistical downscaling algorithm to overcome the lack of observations. The DSCLIM software package implemented by the CERFACS and using a weather typing based statistical methodology will be used as statistical downscaling method to reconstruct the atmospheric variables necessary to force the ISBA-MODCOU hydrological component. The first stage of this work was to estimate and compare the bias and strengths of the two approaches in their ability to reconstruct the past decades. In this sense, SIM hydro-meteorological experiments were performed for some recent years, with a number of observations artificially reduced to a number similar to years 1910, 1930 and 1950. Concurrently, the same recent years have been downscaled by DSCLIM and used to force ISBA-MODCOU. Afterwards, some additional experiments with some modified parameters in the DSCLIM algorithm have been performed in order to adapt the methodology to the study case, and thus trying to improve its performances. Several configurations of the DSCLIM algorithm were applied to the entire century, using the NOAA20CR reanalysis as large-scale predictor. The reconstructed atmospheric variables are compared to the available observations over the entire century to estimate the ability of the statistical downscaling method to reproduce a correct interannual to multidecadal variability. Finally, a novel method is tested: available observations over past decades are introduced in the DSCLIM algorithm, in order to obtain a reconstructed dataset as realistic as possible.

Investigating Over Critical Thresholds of Forest Megafires Danger Conditions in Europe Utilising the ECMWF ERA-Interim Reanalysis

NASA Astrophysics Data System (ADS)

Petroliagkis, Thomas I.; Camia, Andrea; Liberta, Giorgio; Durrant, Tracy; Pappenberger, Florian; San-Miguel-Ayanz, Jesus

2014-05-01

The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) to support the services in charge of the protection of forests against fires in the EU and neighbour countries, and also to provide the EC services and the European Parliament with information on forest fires in Europe. Within its applications, EFFIS provides current and forecast meteorological fire danger maps up to 6 days. Weather plays a key role in affecting wildfire occurrence and behaviour. Meteorological parameters can be used to derive meteorological fire weather indices that provide estimations of fire danger level at a given time over a specified area of interest. In this work, we investigate the suitability of critical thresholds of fire danger to provide an early warning for megafires (fires > 500 ha) over Europe. Past trends of fire danger are analysed computing daily fire danger from weather data taken from re-analysis fields for a period of 31 years (1980 to 2010). Re-analysis global data sets coming from the construction of high-quality climate records, which combine past observations collected from many different observing and measuring platforms, are capable of describing how Fire Danger Indices have evolved over time at a global scale. The latest and most updated ERA-Interim dataset of the European Centre for Medium-Range Weather Forecast (ECMWF) was used to extract meteorological variables needed to compute daily values of the Canadian Fire Weather Index (CFWI) over Europe, with a horizontal resolution of about 75x75 km. Daily time series of CFWI were constructed and analysed over a total of 1,071 European NUTS3 centroids, resulting in a set of percentiles and critical thresholds. Such percentiles could be used as thresholds to help fire services establish a measure of the significance of CFWI outputs as they relate to levels of fire potential, fuel conditions and fire danger. Median percentile values of fire days accumulated over the 31-year period were compared to median values of all days from that period. As expected, the CWFI time series exhibit different values on fire days than on all days. In addition, a percentile analysis was performed in order to determine the behaviour of index values corresponding to fire events falling into the megafire category. This analysis resulted in a set of critical thresholds based on percentiles. By utilising such thresholds, an initial framework of an early warning system has being established. By lowering the value of any of these thresholds, the number of hits could be increased until all extremes were captured (resulting in zero misses). However, in doing so, the number of false alarms tends to increase significantly. Consequently, an optimal trade-off between hits and false alarms has to be established when setting different (critical) CFWI thresholds.

GEOS Atmospheric Model: Challenges at Exascale

NASA Technical Reports Server (NTRS)

Putman, William M.; Suarez, Max J.

2017-01-01

The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.

Atmospheric response to Saharan dust deduced from ECMWF reanalysis increments

NASA Astrophysics Data System (ADS)

Kishcha, P.; Alpert, P.; Barkan, J.; Kirchner, I.; Machenhauer, B.

2003-04-01

This study focuses on the atmospheric temperature response to dust deduced from a new source of data - the European Reanalysis (ERA) increments. These increments are the systematic errors of global climate models, generated in reanalysis procedure. The model errors result not only from the lack of desert dust but also from a complex combination of many kinds of model errors. Over the Sahara desert the dust radiative effect is believed to be a predominant model defect which should significantly affect the increments. This dust effect was examined by considering correlation between the increments and remotely-sensed dust. Comparisons were made between April temporal variations of the ERA analysis increments and the variations of the Total Ozone Mapping Spectrometer aerosol index (AI) between 1979 and 1993. The distinctive structure was identified in the distribution of correlation composed of three nested areas with high positive correlation (> 0.5), low correlation, and high negative correlation (<-0.5). The innermost positive correlation area (PCA) is a large area near the center of the Sahara desert. For some local maxima inside this area the correlation even exceeds 0.8. The outermost negative correlation area (NCA) is not uniform. It consists of some areas over the eastern and western parts of North Africa with a relatively small amount of dust. Inside those areas both positive and negative high correlations exist at pressure levels ranging from 850 to 700 hPa, with the peak values near 775 hPa. Dust-forced heating (cooling) inside the PCA (NCA) is accompanied by changes in the static stability of the atmosphere above the dust layer. The reanalysis data of the European Center for Medium Range Weather Forecast(ECMWF) suggests that the PCA (NCA) corresponds mainly to anticyclonic (cyclonic) flow, negative (positive) vorticity, and downward (upward) airflow. These facts indicate an interaction between dust-forced heating /cooling and atmospheric circulation. The April correlation results are supported by the analysis of vertical distribution of dust concentration, derived from the 24-hour dust prediction system at Tel Aviv University (website: http://earth.nasa.proj.ac.il/dust/current/). For other months the analysis is more complicated because of the essential increasing of humidity along with the northward progress of the ITCZ and the significant impact on the increments.

Mapping the Historical Probability of Increased Flood Hazard During ENSO Events Using a New 20th Century River Flow Reanalysis

NASA Astrophysics Data System (ADS)

Emerton, R.; Cloke, H. L.; Stephens, L.; Woolnough, S. J.; Zsoter, E.; Pappenberger, F.

2016-12-01

El Niño Southern Oscillation (ENSO), a mode of variability which sees fluctuations between anomalously high or low sea surface temperatures in the Pacific, is known to influence river flow and flooding at the global scale. The anticipation and forecasting of floods is crucial for flood preparedness, and this link, alongside the predictive skill of ENSO up to seasons ahead, may provide an early indication of upcoming severe flood events. Information is readily available indicating the likely impacts of El Niño and La Niña on precipitation across the globe, which is often used as a proxy for flood hazard. However, the nonlinearity between precipitation and flood magnitude and frequency means that it is important to assess the impact of ENSO events not only on precipitation, but also on river flow and flooding. Historical probabilities provide key information regarding the likely impacts of ENSO events. We estimate, for the first time, the historical probability of increased flood hazard during El Niño and La Niña through a global hydrological analysis, using a new 20thCentury ensemble river flow reanalysis for the global river network. This dataset was produced by running the ECMWF ERA-20CM atmospheric reanalysis through a research set-up of the Global Flood Awareness System (GloFAS) using the CaMa-Flood hydrodynamic model, to produce a 110-year global reanalysis of river flow. We further evaluate the added benefit of the hydrological analysis over the use of precipitation as a proxy for flood hazard. For example, providing information regarding regions that are likely to experience a lagged influence on river flow compared to the influence on precipitation. Our results map, at the global scale, the probability of abnormally high river flow during any given month during an El Niño or La Niña; information such as this is key for organisations that work at the global scale, such as humanitarian aid organisations, providing a seasons-ahead indicator of potential increased flood hazard that can be used as soon as the event onset is declared, or even earlier, when El Niño or La Niña conditions are first predicted.

Application and Evaluation of an Explicit Prognostic Cloud-Cover Scheme in GRAPES Global Forecast System

NASA Astrophysics Data System (ADS)

Ma, Zhanshan; Liu, Qijun; Zhao, Chuanfeng; Shen, Xueshun; Wang, Yuan; Jiang, Jonathan H.; Li, Zhe; Yung, Yuk

2018-03-01

An explicit prognostic cloud-cover scheme (PROGCS) is implemented into the Global/Regional Assimilation and Prediction System (GRAPES) for global middle-range numerical weather predication system (GRAPES_GFS) to improve the model performance in simulating cloud cover and radiation. Unlike the previous diagnostic cloud-cover scheme (DIAGCS), PROGCS considers the formation and dissipation of cloud cover by physically connecting it to the cumulus convection and large-scale stratiform condensation processes. Our simulation results show that clouds in mid-high latitudes arise mainly from large-scale stratiform condensation processes, while cumulus convection and large-scale condensation processes jointly determine cloud cover in low latitudes. Compared with DIAGCS, PROGCS captures more consistent vertical distributions of cloud cover with the observations from Atmospheric Radiation Measurements (ARM) program at the Southern Great Plains (SGP) site and simulates more realistic diurnal cycle of marine stratocumulus with the ERA-Interim reanalysis data. The low, high, and total cloud covers that are determined via PROGCS appear to be more realistic than those simulated via DIAGCS when both are compared with satellite retrievals though the former maintains slight negative biases. In addition, the simulations of outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) from PROGCS runs have been considerably improved as well, resulting in less biases in radiative heating rates at heights below 850 hPa and above 400 hPa of GRAPES_GFS. Our results indicate that a prognostic method of cloud-cover calculation has significant advantage over the conventional diagnostic one, and it should be adopted in both weather and climate simulation and forecast.

Impact of four-dimensional data assimilation (FDDA) on urban climate analysis

NASA Astrophysics Data System (ADS)

Pan, Linlin; Liu, Yubao; Liu, Yuewei; Li, Lei; Jiang, Yin; Cheng, Will; Roux, Gregory

2015-12-01

This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations.

Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2016-04-01

Traditionally, most of the models for prediction of the atmosphere behavior in the macroweather and climate regimes follow a deterministic approach. However, modern ensemble forecasting systems using stochastic parameterizations are in fact deterministic/ stochastic hybrids that combine both elements to yield a statistical distribution of future atmospheric states. Nevertheless, the result is both highly complex (both numerically and theoretically) as well as being theoretically eclectic. In principle, it should be advantageous to exploit higher level turbulence type scaling laws. Concretely, in the case for the Global Circulation Models (GCM's), due to sensitive dependence on initial conditions, there is a deterministic predictability limit of the order of 10 days. When these models are coupled with ocean, cryosphere and other process models to make long range, climate forecasts, the high frequency "weather" is treated as a driving noise in the integration of the modelling equations. Following Hasselman, 1976, this has led to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well-known are the Linear Inverse Models (LIM). For annual global scale forecasts, they are somewhat superior to the GCM's and have been presented as a benchmark for surface temperature forecasts with horizons up to decades. A key limitation for the LIM approach is that it assumes that the temperature has only short range (exponential) decorrelations. In contrast, an increasing body of evidence shows that - as with the models - the atmosphere respects a scale invariance symmetry leading to power laws with potentially enormous memories so that LIM greatly underestimates the memory of the system. In this talk we show that, due to the relatively low macroweather intermittency, the simplest scaling models - fractional Gaussian noise - can be used for making greatly improved forecasts. The corresponding space-time model (the ScaLIng Macroweather Model (SLIMM) is thus only multifractal in space where the spatial intermittency is associated with different climate zones. SLIMM exploits the power law (scaling) behavior in time of the temperature field and uses the long historical memory of the temperature series to improve the skill. The only model parameter is the fluctuation scaling exponent, H (usually in the range -0.5 - 0), which is directly related to the skill and can be obtained from the data. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5° x 5° regions covering the planet using ERA-Interim, 20CRv2 and NCEP/NCAR reanalysis as reference datasets. We report maps of theoretical skill predicted by the model and we compare it with actual skill based on hindcasts for monthly, seasonal and annual resolutions. We also present maps of calibrated probability hindcasts with their respective validations. Comparisons between our results using SLIMM, some other stochastic autoregressive model, and hindcasts from the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the National Centers for Environmental Prediction (NCEP)'s model CFSv2, are also shown. For seasonal temperature forecasts, SLIMM outperforms the GCM based forecasts in over 90% of the earth's surface. SLIMM forecasts can be accessed online through the site: http://www.to_be_announced.mcgill.ca.

Development of a three-dimensional variational data assimilation system for the Seto Island Sea, Japan

NASA Astrophysics Data System (ADS)

Kurosawa, K.; Uchiyama, Y.

2016-12-01

By optimally combined ocean models with observation data, numerical oceanic reanalysis and forecast systems allow us to predict the ocean more precisely. In general, data assimilation is exploited to prepare the initial condition for the forecast. This technique has widely been employed in atmospheric prediction, whereas oceanic prediction lags behind weather forecast. Accurate oceanic prediction systems have been demanded for operational purposes such as for fisheries, vessel navigation, marine construction, offshore platform management, marine monitoring, etc. In particular, in crowded harbors and estuaries including the Seto Inland Sea (SIS), Japan, data assimilation has seldom been adapted because data from satellites and Argo floats essential to successful oceanic predictions is desperately limited. In addition, although static data assimilation, typically three-dimensional variational data assimilation (3DVAR), is computationally cheap and statistically optimal, but is not physically balanced. For instance, 3DVAR is known to modify velocity and density fields merely mathematically, yet it does not adequately consider quasi-geostrophic balance, which is generally true in most cases. In the present study, we develop a 3DVAR system for Regional Oceanic Modeling Systems (ROMS) and apply to the high-resolution SIS model in a double nested configuration (Kosako et al., 2015). The SIS is the largest estuary in Japan with a number of autonomous in-situ monitoring of vertical profiles of temperature and salinity, tens of tidal gages, along with continuous surface current measurement using HF radars. We first present a theoretical framework of the 3DVAR algorithm by considering geostrophic and thermal-wind balance to find plausible relationships among physical variables to avoid undesirable modifications. Subsequently, the developed 3DVAR is coupled with the SIS ROMS model to compare the model outcomes against some observation data. The 3DVAR ROMS model for the SIS performs much better than the SIS model without assimilation and demonstrates good model skills with reproducing quite complex flows in the SIS because of its complicated topography with more than 3,000 islands in there. Furthermore we will share technical difficulties encountered during the experiment.

Maintaining Atmospheric Mass and Water Balance Within Reanalysis

NASA Technical Reports Server (NTRS)

Takacs, Lawrence L.; Suarez, Max; Todling, Ricardo

2015-01-01

This report describes the modifications implemented into the Goddard Earth Observing System Version-5 (GEOS-5) Atmospheric Data Assimilation System (ADAS) to maintain global conservation of dry atmospheric mass as well as to preserve the model balance of globally integrated precipitation and surface evaporation during reanalysis. Section 1 begins with a review of these global quantities from four current reanalysis efforts. Section 2 introduces the modifications necessary to preserve these constraints within the atmospheric general circulation model (AGCM), the Gridpoint Statistical Interpolation (GSI) analysis procedure, and the Incremental Analysis Update (IAU) algorithm. Section 3 presents experiments quantifying the impact of the new procedure. Section 4 shows preliminary results from its use within the GMAO MERRA-2 Reanalysis project. Section 5 concludes with a summary.

Momentum flux of convective gravity waves derived from an off-line gravity wave parameterization: Spatiotemporal variations at source level

NASA Astrophysics Data System (ADS)

Kang, Min-Jee; Chun, Hye-Yeong; Kim, Young-Ha

2017-04-01

Spatiotemporal variations in momentum flux spectra of convective gravity waves (CGWs) at the source level (cloud top), including nonlinear forcing effects, are examined using an off-line version of CGW parameterization and global reanalysis data. We used 1-hourly NCEP Climate Forecast System Reanalysis (CFSR) forecast data for a period of 32 years (1979-2010), with a horizontal resolution of 1° x1°. The cloud-top momentum flux (CTMF) is not solely proportional to the convective heating rate but is affected by the wave-filtering and resonance factor (WFRF), background stability and temperature underlying the convection. Consequently, the primary peak of CTMF is in the winter hemisphere midlatitude in association with storm-track region where secondary peak of convective heating exists, whereas the secondary peak of CTMF appears in the summer hemisphere tropics and intertropical convergence zone (ITCZ), where primary peak of convective heating exists. The magnitude of CTMF fluctuates largely with 1 year and 1 day periods, commonly in major CTMF regions. At low latitudes and Pacific storm track region, a 6-month period is also significant, and the decadal cycle appears in the Asian summer monsoon region and the Andes Mountains. The equatorial eastern Pacific region exhibits substantial inter-annual to decadal scale of variability with decreasing trend that is described as statistically significant. Interestingly, the correlation between convective heating and the CTMF is relatively lower in the equatorial region than in other regions. The CTMF spectra in the large-CTMF regions reveal that the spectrum shape and width changes with season and location, along with anisotropic shape of the CTMF spectrum, caused by changes in wind speed at the cloud top and the moving speed of convection. The CTMF in the 10°N to 10°S during the period of February to May 2010, when the PreConcordiasi campaign held, approximately follows a lognormal distribution but with a slight underestimation in the tail of the probability density function.

Study on Diagnosing Three Dimensional Cloud Region

NASA Astrophysics Data System (ADS)

Cai, M., Jr.; Zhou, Y., Sr.

2017-12-01

Cloud mask and relative humidity (RH) provided by Cloudsat products from 2007 to 2008 are statistical analyzed to get RH Threshold between cloud and clear sky and its variation with height. A diagnosis method is proposed based on reanalysis data and applied to three-dimensional cloud field diagnosis of a real case. Diagnostic cloud field was compared to satellite, radar and other cloud precipitation observation. Main results are as follows. 1.Cloud region where cloud mask is bigger than 20 has a good space and time corresponding to the high value relative humidity region, which is provide by ECWMF AUX product. Statistical analysis of the RH frequency distribution within and outside cloud indicated that, distribution of RH in cloud at different height range shows single peak type, and the peak is near a RH value of 100%. Local atmospheric environment affects the RH distribution outside cloud, which leads to TH distribution vary in different region or different height. 2. RH threshold and its vertical distribution used for cloud diagnostic was analyzed from Threat Score method. The method is applied to a three dimension cloud diagnosis case study based on NCEP reanalysis data and th diagnostic cloud field is compared to satellite, radar and cloud precipitation observation on ground. It is found that, RH gradient is very big around cloud region and diagnosed cloud area by RH threshold method is relatively stable. Diagnostic cloud area has a good corresponding to updraft region. The cloud and clear sky distribution corresponds to satellite the TBB observations overall. Diagnostic cloud depth, or sum cloud layers distribution consists with optical thickness and precipitation on ground better. The cloud vertical profile reveals the relation between cloud vertical structure and weather system clearly. Diagnostic cloud distribution correspond to cloud observations on ground very well. 3. The method is improved by changing the vertical interval from altitude to temperature. The result shows that, the five factors , including TS score for clear sky, empty forecast, missed forecast, and especially TS score for cloud region and the accurate rate increased obviously. So, the RH threshold and its vertical distribution with temperature is better than with altitude. More tests and comparision should be done to assess the diagnosis method.

Effective Integration of Earth Observation Data and Flood Modeling for Rapid Disaster Response: The Texas 2015 Case

NASA Astrophysics Data System (ADS)

Schumann, G.

2016-12-01

Routinely obtaining real-time 2-D inundation patterns of a flood event at a meaningful spatial resolution and over large scales is at the moment only feasible with either operational aircraft flights or satellite imagery. Of course having model simulations of floodplain inundation available to complement the remote sensing data is highly desirable, for both event re-analysis and forecasting event inundation. Using the Texas 2015 flood disaster, we demonstrate the value of multi-scale EO data for large scale 2-D floodplain inundation modeling and forecasting. A dynamic re-analysis of the Texas 2015 flood disaster was run using a 2-D flood model developed for accurate large scale simulations. We simulated the major rivers entering the Gulf of Mexico and used flood maps produced from both optical and SAR satellite imagery to examine regional model sensitivities and assess associated performance. It was demonstrated that satellite flood maps can complement model simulations and add value, although this is largely dependent on a number of important factors, such as image availability, regional landscape topology, and model uncertainty. In the preferred case where model uncertainty is high, landscape topology is complex (i.e. urbanized coastal area) and satellite flood maps are available (in case of SAR for instance), satellite data can significantly reduce model uncertainty by identifying the "best possible" model parameter set. However, most often the situation is occurring where model uncertainty is low and spatially contiguous flooding can be mapped from satellites easily enough, such as in rural large inland river floodplains. Consequently, not much value from satellites can be added. Nevertheless, where a large number of flood maps are available, model credibility can be increased substantially. In the case presented here this was true for at least 60% of the many thousands of kilometers of river flow length simulated, where satellite flood maps existed. The next steps of this project is to employ a technique termed "targeted observation" approach, which is an assimilation based procedure that allows quantifying the impact observations have on model predictions at the local scale and also along the entire river system, when assimilated with the model at specific "overpass" locations.

Evaluation of land-surface interaction in ECMWF and NCEP/NCAR reanalysis models over grassland (FIFE) and boreal forest (BOREAS)

NASA Astrophysics Data System (ADS)

Betts, Alan K.; Viterbo, Pedro; Beljaars, Anton; Pan, Hua-Lu; Hong, Song-You; Goulden, Mike; Wofsy, Steve

1998-09-01

The National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis models are compared with First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) grassland data from Kansas in 1987 and Boreal Ecosystem-Atmosphere Study (BOREAS) data from an old black spruce site in 1996 near Thompson, Manitoba. Some aspects of the comparison are similar for the two ecosystems. Over grassland and after snowmelt in the boreal forest, both models represent the seasonal cycle of near-surface temperature well. The two models have quite different soil hydrology components. The ECMWF model includes soil water nudging based on low level humidity errors. While this works quite well for the FIFE grassland, it appears to give too high evaporation over the boreal forest. The NCEP/NCAR model constrains long-term drifts by nudging deep soil water toward climatology. Over the FIFE site, this seems to give too low evaporation in midsummer, while at the BOREAS site, evaporation in this model is high. Both models have some difficulty representing the surface diurnal cycle of humidity. In the NCEP/NCAR reanalysis this leads to errors primarily in June, when the surface boundary layer stays saturated and too much precipitation occurs. In the ECMWF reanalysis there is a morning peak of mixing ratio, which an earlier work showed resulted from too shallow a boundary layer in the morning. Over the northern boreal forest there are important physical processes, which are not represented in either reanalysis model. In particular very high model albedos in spring, when there is snow under the forest canopy, lead to a very low daytime net radiation. This in turn leads to a large underestimate of the daytime surface fluxes, particularly the sensible heat flux, and to daytime model surface temperatures that are as much as 15 K low. In addition, the models do not account for the reduction in evaporation associated with frozen soil, and they generally have too large evapotranspiration in June and July, probably because they do not model the tight stomatal control of the coniferous forest.

Improving near-range forecasts of severe precipitation with GNSS and InSAR high-resolution data

NASA Astrophysics Data System (ADS)

Miranda, P. M.; Mateus, P.; Nico, G.; Catalão, J.; Pinto, P.; Tomé, R.; Benevides, P.

2017-12-01

Precipitable water vapor (PWV) maps obtained by GNSS observations are now routinely incorporated into meteorological reanalysis by the main forecast centers such as ECMWF and NCEP. Such data, however, represent a small subset of the available microwave information, which now includes many regional networks of GNSS stations capable to produce frequent updates of the PWV distribution (at least at hourly time scales), and in some cases very high resolution PWV-anomaly fields that may be produced by SAR interferometry (Mateus et al 2013). Such very high resolution fields can be assimilated into state of the art forecast models such as WRF improving it's performance (Mateus et al 2016). In the present study, the assimilation of InSAR data from Sentinel 1A is used to analyse the evolution of two severe precipitation events, which occurred 12 hours apart in the city of Adra in 6-7 September 2015, southern Spain, timed after the two successive passages of the Sentinel. Such events, which produced a flash flood with casualties and large structural damage, were not forecasted by the operational models, but are very accurately reproduced once InSAR data is assimilated, as shown by local observations including weather radar. The physical processes involved in the development of the storm are discussed in some detail, by comparing different simulations: a control run, an experiment with GNSS assimilation, and the experiment with InSAR assimilation. While InSAR images are at this time only available every 6 days, the fact that an improvement of the water vapor distribution by data assimilation can have such a dramatic impact in severe weather forecasts suggests there is significant room for improvement in near term forecasting, by a better incorporation of both higher resolution GNSS data and more frequent SAR images.

Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems

NASA Astrophysics Data System (ADS)

Fujiwara, Masatomo; Wright, Jonathon S.; Manney, Gloria L.; Gray, Lesley J.; Anstey, James; Birner, Thomas; Davis, Sean; Gerber, Edwin P.; Harvey, V. Lynn; Hegglin, Michaela I.; Homeyer, Cameron R.; Knox, John A.; Krüger, Kirstin; Lambert, Alyn; Long, Craig S.; Martineau, Patrick; Molod, Andrea; Monge-Sanz, Beatriz M.; Santee, Michelle L.; Tegtmeier, Susann; Chabrillat, Simon; Tan, David G. H.; Jackson, David R.; Polavarapu, Saroja; Compo, Gilbert P.; Dragani, Rossana; Ebisuzaki, Wesley; Harada, Yayoi; Kobayashi, Chiaki; McCarty, Will; Onogi, Kazutoshi; Pawson, Steven; Simmons, Adrian; Wargan, Krzysztof; Whitaker, Jeffrey S.; Zou, Cheng-Zhi

2017-01-01

The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue The SPARC Reanalysis Intercomparison Project (S-RIP) in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports.

Synoptic thermodynamic and dynamic patterns associated with Quitandinha River flooding events in Petropolis, Rio de Janeiro (Brazil)

NASA Astrophysics Data System (ADS)

da Silva, Fabricio Polifke; Justi da Silva, Maria Gertrudes Alvarez; Rotunno Filho, Otto Corrêa; Pires, Gisele Dornelles; Sampaio, Rafael João; de Araújo, Afonso Augusto Magalhães

2018-05-01

Natural disasters are the result of extreme or intense natural phenomena that cause severe impacts on society. These impacts can be mitigated through preventive measures that can be aided by better knowledge of extreme phenomena and monitoring of forecasting and alert systems. The city of Petropolis (in a mountainous region of the state of Rio de Janeiro, Brazil) is prone to heavy rain events, often leading to River overflows, landslides, and loss of life. In that context, this work endeavored to characterize the thermodynamic and dynamic synoptic patterns that trigger heavy rainfall episodes and the corresponding flooding of Quitandinha River. More specifically, we reviewed events from the time period between January 2013 and December 2014 using reanalysis data. We expect that the overall description obtained of synoptic patterns should provide adequate qualitative aid to the decision-making processes involved in operational forecasting procedures. We noticed that flooding events were related to the presence of the South Atlantic Convergence Zone (SACZ), frontal systems (FS), and convective storms (CS). These systems showed a similar behavior on high-frequency wind components, notably with respect to northwest winds before precipitation and to a strong southwest wind component during rainfall events. Clustering analyses indicated that the main component for precipitation formation with regard to CS systems comes from daytime heating, with the dynamic component presenting greater efficiency for the FS configurations. The SACZ events were influenced by moisture availability along the vertical column of the atmosphere and also due to dynamic components of precipitation efficiency and daytime heating, the latter related to the continuous transport of moisture from the Amazon region and South Atlantic Ocean towards Rio de Janeiro state.

A Comparison of Synoptic Classification Methods for Application to Wind Power Prediction

NASA Astrophysics Data System (ADS)

Fowler, P.; Basu, S.

2008-12-01

Wind energy is a highly variable resource. To make it competitive with other sources of energy for integration on the power grid, at the very least, a day-ahead forecast of power output must be available. In many grid operations worldwide, next-day power output is scheduled in 30 minute intervals and grid management routinely occurs at real time. Maintenance and repairs require costly time to complete and must be scheduled along with normal operations. Revenue is dependent on the reliability of the entire system. In other words, there is financial and managerial benefit to short-term prediction of wind power. One approach to short-term forecasting is to combine a data centric method such as an artificial neural network with a physically based approach like numerical weather prediction (NWP). The key is in associating high-dimensional NWP model output with the most appropriately trained neural network. Because neural networks perform the best in the situations they are designed for, one can hypothesize that if one can identify similar recurring states in historical weather data, this data can be used to train multiple custom designed neural networks to be used when called upon by numerical prediction. Identifying similar recurring states may offer insight to how a neural network forecast can be improved, but amassing the knowledge and utilizing it efficiently in the time required for power prediction would be difficult for a human to master, thus showing the advantage of classification. Classification methods are important tools for short-term forecasting because they can be unsupervised, objective, and computationally quick. They primarily involve categorizing data sets in to dominant weather classes, but there are numerous ways to define a class and a great variety in interpretation of the results. In the present study a collection of classification methods are used on a sampling of atmospheric variables from the North American Regional Reanalysis data set. The results will be discussed in relation to their use for short-term wind power forecasting by neural networks.

Global Eddy-Permitting Ocean Reanalyses and Simulations of the Period 1992 to Present

NASA Astrophysics Data System (ADS)

Parent, L.; Ferry, N.; Barnier, B.; Garric, G.; Bricaud, C.; Testut, C.-E.; Le Galloudec, O.; Lellouche, J.-M.; Greiner, E.; Drevillon, M.; Remy, E.; Moulines, J.-M.; Guinehut, S.; Cabanes, C.

2013-09-01

We present GLORYS2V1 global ocean and sea-ice eddy permitting reanalysis over the altimetric era (1993- 2009). This reanalysis is based on an ocean and sea-ice general circulation model at 1⁄4° horizontal resolution assimilating sea surface temperature, in situ profiles of temperature and salinity and along-track sea level anomaly observations. The reanalysis has been produced along with a reference simulation called MJM95 which allows evaluating the benefits of the data assimilation. In the introduction, we briefly describe the GLORYS2V1 reanalysis system. In sections 2, 3 and 4, the reanalysis skill is presented. Data assimilation diagnostics reveal that the reanalysis is stable all along the time period, with however an improved skill when Argo observation network establishes. GLORYS2V1 captures well climate signals and trends and describes meso-scale variability in a realistic manner.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2018-06-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2017-08-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Assimilation of Satellite Sea Surface Salinity Fields: Validating Ocean Analyses and Identifying Errors in Surface Buoyancy Fluxes

NASA Astrophysics Data System (ADS)

Mehra, A.; Nadiga, S.; Bayler, E. J.; Behringer, D.

2014-12-01

Recently available satellite sea-surface salinity (SSS) fields provide an important new global data stream for assimilation into ocean forecast systems. In this study, we present results from assimilating satellite SSS fields from NASA's Aquarius mission into the National Oceanic and Atmospheric Administration's (NOAA) operational Modular Ocean Model version 4 (MOM4), the oceanic component of NOAA's operational seasonal-interannual Climate Forecast System (CFS). Experiments on the sensitivity of the ocean's overall state to different relaxation time periods were run to evaluate the importance of assimilating high-frequency (daily to mesoscale) and low-frequency (seasonal) SSS variability. Aquarius SSS data (Aquarius Data Processing System (ADPS) version 3.0), mapped daily fields at 1-degree spatial resolution, were used. Four model simulations were started from the same initial ocean condition and forced with NOAA's daily Climate Forecast System Reanalysis (CFSR) fluxes, using a relaxation technique to assimilate daily satellite sea surface temperature (SST) fields and selected SSS fields, where, except as noted, a 30-day relaxation period is used. The simulations are: (1) WOAMC, the reference case and similar to the operational setup, assimilating monthly climatological SSS from the 2009 NOAA World Ocean Atlas; (2) AQ_D, assimilating daily Aquarius SSS; (3) AQ_M, assimilating monthly Aquarius SSS; and (4) AQ_D10, assimilating daily Aquarius SSS, but using a 10-day relaxation period. The analysis focuses on the tropical Pacific Ocean, where the salinity dynamics are intense and dominated by El Niño interannual variability in the cold tongue region and by high-frequency precipitation events in the western Pacific warm pool region. To assess the robustness of results and conclusions, we also examine the results for the tropical Atlantic and Indian Oceans. Preliminary validation studies are conducted using observations, such as satellite sea-surface height (SSH) fields and in situ Argo buoy vertical profiles of temperature and salinity, to demonstrate that SSS data assimilation improves ocean state representation of the following variables: ocean heat content (0-300m), dynamic height (0-1000m), mixed-layer depth, sea surface heigh, and surface buoyancy fluxes.

Evaluation of the Ability of S2S and NMME Models to Predict Heat Waves Following Drought Events in the United States

NASA Astrophysics Data System (ADS)

Ford, T.; Dirmeyer, P.

2016-12-01

The influence of antecedent drought conditions on the onset of heat waves in North America is important as the establishment of past heat wave events has been connected to both advection of warm, dry air and limitation of local moisture recycling due to dry soils. The strong connection between the land surface and subsequent extreme heat offers promise that realistic soil moisture initialization could improve model forecast skill. However, there is still a lack of consensus about the (1) the role of antecedent drought conditions in forcing heat waves over North America and (2) the ability of numerical forecast models to predict extreme heat events at sub-seasonal to seasonal time scales. For this project, we use atmospheric reanalysis datasets to establish the connection between drought and subsequent extreme heat events. The Standardized Precipitation Index (SPI), computed over 30-, 60-, and 90-day intervals, is used to identify drought events, while the excess heat factor defines subsequent heat wave events. We focus on heat waves immediately following drought periods, including events coinciding with but not beginning prior to the start of drought, as well as heat wave events beginning no more than 3 days after the demise of a drought event. Hindcasts from individual model ensemble members of the Sub-seasonal to Seasonal Prediction (S2S) Project and the Phase II of the North American Multi-Model Ensemble (NMME) are assessed with regard to heat wave prediction. Each individual S2S and NMME ensemble member is evaluated to determine if their respective hindcasts are able to capture/predict heat wave events identified in the reanalysis products.

A High-resolution Reanalysis for the European CORDEX Region

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Bollmeyer, Christoph; Crewell, Susanne; Friederichs, Petra; Hense, Andreas; Keller, Jan; Keune, Jessica; Kneifel, Stefan; Ohlwein, Christian; Pscheidt, Ieda; Redl, Stephanie; Steinke, Sandra

2014-05-01

A High-resolution Reanalysis for the European CORDEX Region Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. The work presented here focuses on the regional reanalysis for Europe with a domain matching the CORDEX-EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km). The COSMO reanalysis system comprises the assimilation of observational data using the existing nudging scheme of COSMO and is complemented by a special soil moisture analysis and boundary conditions given by ERA-interim data. The reanalysis data set currently covers 6 years (2007-2012). The evaluation of the reanalyses is done using independent observations with special emphasis on precipitation and high-impact weather situations. The development and evaluation of the COSMO-based reanalysis for the CORDEX-Euro domain can be seen as a preparation for joint European activities on the development of an ensemble system of regional reanalyses for Europe.

The CMEMS-Med-MFC-Biogeochemistry operational system: implementation of NRT and Multi-Year validation tools

NASA Astrophysics Data System (ADS)

Salon, Stefano; Cossarini, Gianpiero; Bolzon, Giorgio; Teruzzi, Anna

2017-04-01

The Mediterranean Monitoring and Forecasting Centre (Med-MFC) is one of the regional production centres of the EU Copernicus Marine Environment Monitoring Service (CMEMS). Med-MFC manages a suite of numerical model systems for the operational delivery of the CMEMS products, providing continuous monitoring and forecasting of the Mediterranean marine environment. The CMEMS products of fundamental biogeochemical variables (chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, primary productivity, pH, pCO2) are organised as gridded datasets and are available at the marine.copernicus.eu web portal. Quantitative estimates of CMEMS products accuracy are prerequisites to release reliable information to intermediate users, end users and to other downstream services. In particular, validation activities aim to deliver accuracy information of the model products and to serve as a long term monitoring of the performance of the modelling systems. The quality assessment of model output is implemented using a multiple-stages approach, basically inspired to the classic "GODAE 4 Classes" metrics and criteria (consistency, quality, performance and benefit). Firstly, pre-operational runs qualify the operational model system against historical data, also providing a verification of the improvements of the new model system release with respect to the previous version. Then, the near real time (NRT) validation aims at delivering a sustained on-line skill assessment of the model analysis and forecast, relying on the NRT available relevant observations (e.g. in situ, Bio Argo and satellite observations). NRT validation results are operated on weekly basis and published on the MEDEAF web portal (www.medeaf.inogs.it). On a quarterly basis, the integration of the NRT validation activities delivers a comprehensive view of the accuracy of model forecast through the official CMEMS validation webpage. Multi-Year production (e.g. reanalysis runs) follows a similar procedure, and the validation is achieved using the same metrics on available historical observations (e.g. the World Ocean Atlas 2013 dataset). Results of the validation activities show that the comparison of the different variables of the CMEMS products with experimental data is feasible at different levels (i.e. either as skill assessment of the short-term forecast and as model consistency through different system versions) and at different spatial and temporal scales. In particular, the accuracy of some variables (chlorophyll, nitrate, oxygen) can be provided at weekly scale and sub-mesoscale, others (carbonate system, phosphate) at quarterly/annual and sub-basin scale, and others (phytoplankton biomass, primary production) only at the level of consistency of model functioning (e.g. literature- or climatology-based). In spite of a wide literature on model validation has been produced so far, maintaining a validation framework in the biogeochemical operational contest that fulfils GODAE criteria is still a challenge. Recent results of the validation activities and new potential validation framework at the Med-MFC will be presented in our contribution.

Oceanic influence on seasonal malaria outbreaks over Senegal and Sahel

NASA Astrophysics Data System (ADS)

Diouf, Ibrahima; Rodríguez de Fonseca, Belen; Deme, Abdoulaye; Cisse Cisse, Moustapha; Ndione Ndione, Jaques-Andre; Gaye, Amadou T.; Suarez, Roberto

2015-04-01

Beyond assessment and analysis of observed and simulated malaria parameters, this study is furthermore undertaken in the framework of predictability of malaria outbreaks in Senegal and remote regions in Sahel, which are found to take place two months after the rainy season. The predictors are the sea surface temperature anomalous patterns at different ocean basins mainly over the Pacific and Atlantic as they are related to changes in air temperature, humidity, rainfall and wind. A relationship between El Niño and anomalous malaria parameters is found. The malaria parameters are calculated with the Liverpool Malaria Model (LMM) using meteorological datasets from different reanalysis products. A hindcast of these parameters is performed using the Sea Surface temperature based Statistical Seasonal ForeCAST (S4CAST) model developed at UCM in order to predict malaria parameters some months in advance. The results of this work will be useful for decision makers to better access to climate forecasts and application on malaria transmission risk.

Improved simulation of precipitation in the tropics using a modified BMJ scheme in the WRF model

NASA Astrophysics Data System (ADS)

Fonseca, R. M.; Zhang, T.; Yong, K.-T.

2015-09-01

The successful modelling of the observed precipitation, a very important variable for a wide range of climate applications, continues to be one of the major challenges that climate scientists face today. When the Weather Research and Forecasting (WRF) model is used to dynamically downscale the Climate Forecast System Reanalysis (CFSR) over the Indo-Pacific region, with analysis (grid-point) nudging, it is found that the cumulus scheme used, Betts-Miller-Janjić (BMJ), produces excessive rainfall suggesting that it has to be modified for this region. Experimentation has shown that the cumulus precipitation is not very sensitive to changes in the cloud efficiency but varies greatly in response to modifications of the temperature and humidity reference profiles. A new version of the scheme, denoted "modified BMJ" scheme, where the humidity reference profile is more moist, was developed. In tropical belt simulations it was found to give a better estimate of the observed precipitation as given by the Tropical Rainfall Measuring Mission (TRMM) 3B42 data set than the default BMJ scheme for the whole tropics and both monsoon seasons. In fact, in some regions the model even outperforms CFSR. The advantage of modifying the BMJ scheme to produce better rainfall estimates lies in the final dynamical consistency of the rainfall with other dynamical and thermodynamical variables of the atmosphere.

Development of fine-resolution analyses and expanded large-scale forcing properties. Part I: Methodology and evaluation

DOE PAGES

Li, Zhijin; Vogelmann, Andrew M.; Feng, Sha; ...

2015-01-20

We produce fine-resolution, three-dimensional fields of meteorological and other variables for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The Community Gridpoint Statistical Interpolation system is implemented in a multiscale data assimilation (MS-DA) framework that is used within the Weather Research and Forecasting model at a cloud-resolving resolution of 2 km. The MS-DA algorithm uses existing reanalysis products and constrains fine-scale atmospheric properties by assimilating high-resolution observations. A set of experiments show that the data assimilation analysis realistically reproduces the intensity, structure, and time evolution of clouds and precipitation associated with a mesoscale convective system.more » Evaluations also show that the large-scale forcing derived from the fine-resolution analysis has an overall accuracy comparable to the existing ARM operational product. For enhanced applications, the fine-resolution fields are used to characterize the contribution of subgrid variability to the large-scale forcing and to derive hydrometeor forcing, which are presented in companion papers.« less

Downscaling, 2-way Nesting, and Data Assimilative Modeling in Coastal and Shelf Waters of the U.S. Mid-Atlantic Bight and Gulf of Maine

NASA Astrophysics Data System (ADS)

Wilkin, J.; Levin, J.; Lopez, A.; Arango, H.

2016-02-01

Coastal ocean models that downscale output from basin and global scale models are widely used to study regional circulation at enhanced resolution and locally important ecosystem, biogeochemical, and geomorphologic processes. When operated as now-cast or forecast systems, these models offer predictions that assist decision-making for numerous maritime applications. We describe such a system for shelf waters of the Mid-Atlantic Bight (MAB) and Gulf of Maine (GoM) where the MARACOOS and NERACOOS associations of U.S. IOOS operate coastal ocean observing systems that deliver a dense observation set using CODAR HF-radar, autonomous underwater glider vehicles (AUGV), telemetering moorings, and drifting buoys. Other U.S. national and global observing systems deliver further sustained observations from moorings, ships, profiling floats, and a constellation of satellites. Our MAB and GoM re-analysis and forecast system uses the Regional Ocean Modeling System (ROMS; myroms.org) with 4-dimensional Variational (4D-Var) data assimilation to adjust initial conditions, boundary conditions, and surface forcing in each analysis cycle. Data routinely assimilated include CODAR velocities, altimeter satellite sea surface height (with coastal corrections), satellite temperature, in situ CTD data from AUGV and ships (NMFS Ecosystem Monitoring voyages), and all in situ data reported via the WMO GTS network. A climatological data assimilative analysis of hydrographic and long-term mean velocity observations specifies the regional Mean Dynamic Topography that augments altimeter sea level anomaly data and is also used to adjust boundary condition biases that would otherwise be introduced in the process of downscaling from global models. System performance is described with respect to the impact of satellite, CODAR and in situ observations on analysis skill. Results from a 2-way nested modeling system that adds enhanced resolution over the NSF OOI Pioneer Array in the central MAB are also shown.

Development of Gridded Innovations and Observations Supplement to MERRA-2

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Da Silva, Arlindo M.; Robertson, Franklin R.

2017-01-01

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths can be known, drift in certain instruments and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are in observation-space data formats and have not been made easily available to reanalysis users.A test data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). We refer to this proof-of-concept data as the MERRA-2 Gridded Innovations and Observations (GIO). In this paper, we present the data format and its strengths and limitations with some initial testing and validation of the methodology.

Atmospheric response to Saharan dust deduced from ECMWF reanalysis (ERA) temperature increments

NASA Astrophysics Data System (ADS)

Kishcha, P.; Alpert, P.; Barkan, J.; Kirchner, I.; Machenhauer, B.

2003-09-01

This study focuses on the atmospheric temperature response to dust deduced from a new source of data the European Reanalysis (ERA) increments. These increments are the systematic errors of global climate models, generated in the reanalysis procedure. The model errors result not only from the lack of desert dust but also from a complex combination of many kinds of model errors. Over the Sahara desert the lack of dust radiative effect is believed to be a predominant model defect which should significantly affect the increments. This dust effect was examined by considering correlation between the increments and remotely sensed dust. Comparisons were made between April temporal variations of the ERA analysis increments and the variations of the Total Ozone Mapping Spectrometer aerosol index (AI) between 1979 and 1993. The distinctive structure was identified in the distribution of correlation composed of three nested areas with high positive correlation (>0.5), low correlation and high negative correlation (<-0.5). The innermost positive correlation area (PCA) is a large area near the center of the Sahara desert. For some local maxima inside this area the correlation even exceeds 0.8. The outermost negative correlation area (NCA) is not uniform. It consists of some areas over the eastern and western parts of North Africa with a relatively small amount of dust. Inside those areas both positive and negative high correlations exist at pressure levels ranging from 850 to 700 hPa, with the peak values near 775 hPa. Dust-forced heating (cooling) inside the PCA (NCA) is accompanied by changes in the static instability of the atmosphere above the dust layer. The reanalysis data of the European Center for Medium Range Weather Forecast (ECMWF) suggest that the PCA (NCA) corresponds mainly to anticyclonic (cyclonic) flow, negative (positive) vorticity and downward (upward) airflow. These findings are associated with the interaction between dust-forced heating/cooling and atmospheric circulation. This paper contributes to a better understanding of dust radiative processes missed in the model.

Smart Climatology System

DTIC Science & Technology

2010-09-24

12 2.1 Downscaling /Reanalysis Data ................................................................................ 12 2.2 Downscaling of...Comparison of Resolutions of Maximum Significant Wave heights for La Niña >= 8 ft >= 6 ft 12 2 Data Production Issues 2.1 Downscaling /Reanalysis...numerical weather prediction systems. The usage of satellite data , for example, is markedly different than the past practice. This played havoc with

The Climate Signal in Regional Moisture Fluxes: A Comparison of Three Global Data Assimilation Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.

1997-01-01

This study assesses the quality of estimates of climate variability in moisture flux and convergence from three assimilated data sets: two are reanalysis products generated at the Goddard Data Assimilation Office (DAO) and the National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEPJNCAR), and the third consists of the operational analyses generated at the European Center for Medium Range Forecasts (ECMWF). The regions under study (the United States Great Plains, the Indian monsoon region, and Argentina east of the Andes) are characterized by frequent low level jets (LLJs) and other interannual low level wind variations tied to the large-scale flow. While the emphasis is on the reanalysis products, the comparison with the operational product is provided to help assess the improvements gained from a fixed analysis system. All three analyses capture the main moisture flux anomalies associated with selected extreme climate (drought and flood) events during the period 1985-93. The correspondence is strongest over the Great Plains and weakest over the Indian monsoon region reflecting differences in the observational coverage. For the reanalysis products, the uncertainties in the lower tropospheric winds is by far the dominant source of the discrepancies in the moisture flux anomalies in the middle latitude regions. Only in the Indian Monsoon region, where interannual variability in the low level winds is comparatively small, does the moisture bias play a substantial role. In contrast, the comparisons with the operational product show differences in moisture which are comparable torhe differences in the wind in all three regions. Compared with the fluxes, the anomalous moisture convergences show substantially larger differences among the three products. The best agreement occurs over the Great Plains region where all three products show vertically-integrated moisture convergence during the floods and divergence during the drought with differences in magnitude of about 25%. The reanalysis products, in particular, show good agreement in depicting the different roles of the mean flow and transients during the flood and drought periods. Differences between the three products in the other two regions exceed 100% reflecting differences in the low level jets and the large scale circulation patterns. The operational product tends to have locally larger amplitude convergence fields which average out in area-mean budgets: this appears to be at least in part due to errors in the surface pressure fields and aliasing from the higher resolution of the original ECMWF fields. On average, the reanalysis products show higher coherence with each other than with the operational product in the estimates of interannual variability. This result is less clear in the Indian monsoon region where differences in the input observations appears to be an important factor. The agreement in the anomalous convergence patterns is, however, still rather poor even over relatively data dense regions such as the United States Great Plains. These differences are attributed to deficiencies in the assimilating GCM's representations of the planetary boundary layer and orography, and a global observing system incapable of resolving the highly confined low level winds associated with the climate anomalies.

Sampling the Vertical Moisture Structure of an Atmospheric River Event Using Airborne GPS Radio Occultation Profiling

NASA Astrophysics Data System (ADS)

Haase, J. S.; Malloy, K.; Murphy, B.; Sussman, J.; Zhang, W.

2015-12-01

Atmospheric rivers (ARs) are of high concern in California, bringing significant rain to the region over extended time periods of up to 5 days, potentially causing floods, and more importantly, contributing to the Sierra snowpack that provides much of the regional water resources. The CalWater project focuses on predicting the variability of the West Coast water supply, including improving AR forecasting. Unfortunately, data collection over the ocean remains a challenge and impacts forecasting accuracy. One novel technique to address this issue includes airborne GPS radio occultation (ARO), using broadcast GPS signals from space to measure the signal ray path bending angle and refractivity to retrieve vertical water vapor profiles. The Global Navigation Satellite System Instrument System for Multistatic and Occultation Sensing (GISMOS) system was developed for this purpose for recording and processing high-sample rate (10MHz) signals in the lower troposphere. Previous studies (Murphy et al, 2014) have shown promising results in acquiring airborne GPS RO data, comparing it to dropsondes and numerical weather models. CalWater launched a field campaign in the beginning of 2015 which included testing GISMOS ARO on the NOAA GIV aircraft for AR data acquisition, flying into the February 6th AR event that brought up to 35 cm of rain to central California. This case study will compare airborne GPS RO refractivity profiles to the NCEP-NCAR final reanalysis model and dropsonde profiles. We will show the data distribution and explain the sampling characteristics, providing high resolution vertical information to the sides of the aircraft in a manner complementary to dropsondes beneath the flight track. We will show how this method can provide additional reliable data during the development of AR storms.

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

NASA Astrophysics Data System (ADS)

van Noije, T. P. C.; Le Sager, P.; Segers, A. J.; van Velthoven, P. F. J.; Krol, M. C.; Hazeleger, W.

2014-03-01

We have integrated the atmospheric chemistry and transport model TM5 into the global climate model EC-Earth version 2.4. We present an overview of the TM5 model and the two-way data exchange between TM5 and the integrated forecasting system (IFS) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), the atmospheric general circulation model of EC-Earth. In this paper we evaluate the simulation of tropospheric chemistry and aerosols in a one-way coupled configuration. We have carried out a decadal simulation for present-day conditions and calculated chemical budgets and climatologies of tracer concentrations and aerosol optical depth. For comparison we have also performed offline simulations driven by meteorological fields from ECMWF's ERA-Interim reanalysis and output from the EC-Earth model itself. Compared to the offline simulations, the online-coupled system produces more efficient vertical mixing in the troposphere, which likely reflects an improvement of the treatment of cumulus convection. The chemistry in the EC-Earth simulations is affected by the fact that the current version of EC-Earth produces a cold bias with too dry air in large parts of the troposphere. Compared to the ERA-Interim driven simulation, the oxidizing capacity in EC-Earth is lower in the tropics and higher in the extratropics. The methane lifetime is 7% higher in EC-Earth, but remains well within the range reported in the literature. We evaluate the model by comparing the simulated climatologies of surface carbon monoxide, tropospheric and surface ozone, and aerosol optical depth against observational data. The work presented in this study is the first step in the development of EC-Earth into an Earth system model with fully interactive atmospheric chemistry and aerosols.

Synoptic Scale North American Weather Tracks and the Formation of North Atlantic Windstorms

NASA Astrophysics Data System (ADS)

Baum, A. J.; Godek, M. L.

2014-12-01

Each winter, dozens of fatalities occur when intense North Atlantic windstorms impact Western Europe. Forecasting the tracks of these storms in the short term is often problematic, but long term forecasts provide an even greater challenge. Improved prediction necessitates the ability to identify these low pressure areas at formation and understand commonalities that distinguish these storms from other systems crossing the Atlantic, such as where they develop. There is some evidence that indicates the majority of intense windstorms that reach Europe have origins far west, as low pressure systems that develop over the North American continent. This project aims to identify the specific cyclogenesis regions in North America that produce a significantly greater number of dangerous storms. NOAA Ocean Prediction Center surface pressure reanalysis maps are used to examine the tracks of storms. Strong windstorms are characterized by those with a central pressure of less than 965 hPa at any point in their life cycle. Tracks are recorded using a coding system based on source region, storm track and dissipation region. The codes are analyzed to determine which region contains the most statistical significance with respect to strong Atlantic windstorm generation. The resultant set of codes also serves as a climatology of North Atlantic extratropical cyclones. Results indicate that a number of windstorms favor cyclogenesis regions off the east coast of the United States. A large number of strong storms that encounter east coast cyclogenesis zones originate in the central mountain region, around Colorado. These storms follow a path that exits North America around New England and subsequently travel along the Canadian coast. Some of these are then primed to become "bombs" over the open Atlantic Ocean.

Analysing the teleconnection systems affecting the climate of the Carpathian Basin

NASA Astrophysics Data System (ADS)

Kristóf, Erzsébet; Bartholy, Judit; Pongrácz, Rita

2017-04-01

Nowadays, the increase of the global average near-surface air temperature is unequivocal. Atmospheric low-frequency variabilities have substantial impacts on climate variables such as air temperature and precipitation. Therefore, assessing their effects is essential to improve global and regional climate model simulations for the 21st century. The North Atlantic Oscillation (NAO) is one of the best-known atmospheric teleconnection patterns affecting the Carpathian Basin in Central Europe. Besides NAO, we aim to analyse other interannual-to-decadal teleconnection patterns, which might have significant impacts on the Carpathian Basin, namely, the East Atlantic/West Russia pattern, the Scandinavian pattern, the Mediterranean Oscillation, and the North-Sea Caspian Pattern. For this purpose primarily the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA-20C atmospheric reanalysis dataset and multivariate statistical methods are used. The indices of each teleconnection pattern and their correlations with temperature and precipitation will be calculated for the period of 1961-1990. On the basis of these data first the long range (i. e. seasonal and/or annual scale) forecast ability is evaluated. Then, we aim to calculate the same indices of the relevant teleconnection patterns for the historical and future simulations of Coupled Model Intercomparison Project Phase 5 (CMIP5) models and compare them against each other using statistical methods. Our ultimate goal is to examine all available CMIP5 models and evaluate their abilities to reproduce the selected teleconnection systems. Thus, climate predictions for the 21st century for the Carpathian Basin may be improved using the best-performing models among all CMIP5 model simulations.

Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice-ocean data assimilation system

NASA Astrophysics Data System (ADS)

Nakanowatari, Takuya; Inoue, Jun; Sato, Kazutoshi; Bertino, Laurent; Xie, Jiping; Matsueda, Mio; Yamagami, Akio; Sugimura, Takeshi; Yabuki, Hironori; Otsuka, Natsuhiko

2018-06-01

Accelerated retreat of Arctic Ocean summertime sea ice has focused attention on the potential use of the Northern Sea Route (NSR), for which sea ice thickness (SIT) information is crucial for safe maritime navigation. This study evaluated the medium-range (lead time below 10 days) forecast of SIT distribution in the East Siberian Sea (ESS) in early summer (June-July) based on the TOPAZ4 ice-ocean data assimilation system. A comparison of the operational model SIT data with reliable SIT estimates (hindcast, satellite and in situ data) showed that the TOPAZ4 reanalysis qualitatively reproduces the tongue-like distribution of SIT in ESS in early summer and the seasonal variations. Pattern correlation analysis of the SIT forecast data over 3 years (2014-2016) reveals that the early summer SIT distribution is accurately predicted for a lead time of up to 3 days, but that the prediction accuracy drops abruptly after the fourth day, which is related to a dynamical process controlled by synoptic-scale atmospheric fluctuations. For longer lead times ( > 4 days), the thermodynamic melting process takes over, which contributes to most of the remaining prediction accuracy. In July 2014, during which an ice-blocking incident occurred, relatively thick SIT ( ˜ 150 cm) was simulated over the ESS, which is consistent with the reduction in vessel speed. These results suggest that TOPAZ4 sea ice information has great potential for practical applications in summertime maritime navigation via the NSR.

Rainfall forecast in the Upper Mahaweli basin in Sri Lanka using RegCM model

NASA Astrophysics Data System (ADS)

Muhammadh, K. M.; Mafas, M. M. M.; Weerakoon, S. B.

2017-04-01

The Upper Mahaweli basin is the upper most sub basin of 788 km2 in size above Polgolla barrage in the Mahaweli River, the longest river in Sri Lanka which starts from the central hills of the island and drains to the sea at the North-east coast. Rainfall forecast in the Upper Mahaweli basin is important for issuing flood warning in the river downstream of the reservoirs, landslide warning in the settlements in hilly areas. Anticipatory water management in the basin including reservoir operations, barrage gate operation for releasing water for irrigation and flood control also require reliable rainfall and runoff prediction in the sub basin. In this study, the Regional Climate Model (RegCM V4.4.5.11) is calibrated for the basin to dynamically downscale reanalysis weather data of Global Climate Model (GCM) to forecast the rainfall in the basin. Observed rainfalls at gauging stations within the basin were used for model calibration and validation. The observed rainfall data was analysed using ARC GIS and the output of RegCM was analysed using GrADS tool. The output of the model and the observed precipitation were obtained on grids of size 0.1 degrees and the accuracy of the predictions were analysed using RMSE and Mean Model Absolute Error percentage (MAME %). The predictions by the calibrated RegCM model for the basin is shown to be satisfactory. The model is a useful tool for rainfall forecast in the Upper Mahaweli River basin.

Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations

NASA Astrophysics Data System (ADS)

Sato, Kazutoshi; Inoue, Jun; Yamazaki, Akira; Kim, Joo-Hong; Maturilli, Marion; Dethloff, Klaus; Hudson, Stephen R.; Granskog, Mats A.

2017-02-01

Recent cold winter extremes over Eurasia and North America have been considered to be a consequence of a warming Arctic. More accurate weather forecasts are required to reduce human and socioeconomic damages associated with severe winters. However, the sparse observing network over the Arctic brings errors in initializing a weather prediction model, which might impact accuracy of prediction results at midlatitudes. Here we show that additional Arctic radiosonde observations from the Norwegian young sea ICE expedition (N-ICE2015) drifting ice camps and existing land stations during winter improved forecast skill and reduced uncertainties of weather extremes at midlatitudes of the Northern Hemisphere. For two winter storms over East Asia and North America in February 2015, ensemble forecast experiments were performed with initial conditions taken from an ensemble atmospheric reanalysis in which the observation data were assimilated. The observations reduced errors in initial conditions in the upper troposphere over the Arctic region, yielding more precise prediction of the locations and strengths of upper troughs and surface synoptic disturbances. Errors and uncertainties of predicted upper troughs at midlatitudes would be brought with upper level high potential vorticity (PV) intruding southward from the observed Arctic region. This is because the PV contained a "signal" of the additional Arctic observations as it moved along an isentropic surface. This suggests that a coordinated sustainable Arctic observing network would be effective not only for regional weather services but also for reducing weather risks in locations distant from the Arctic.

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

High-resolution modeling of coastal freshwater discharge and glacier mass balance in the Gulf of Alaska watershed

NASA Astrophysics Data System (ADS)

Beamer, J. P.; Hill, D. F.; Arendt, A.; Liston, G. E.

2016-05-01

A comprehensive study of the Gulf of Alaska (GOA) drainage basin was carried out to improve understanding of the coastal freshwater discharge (FWD) and glacier volume loss (GVL). Hydrologic processes during the period 1980-2014 were modeled using a suite of physically based, spatially distributed weather, energy-balance snow/ice melt, soil water balance, and runoff routing models at a high-resolution (1 km horizontal grid; daily time step). Meteorological forcing was provided by the North American Regional Reanalysis (NARR), Modern Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR) data sets. Streamflow and glacier mass balance modeled using MERRA and CFSR compared well with observations in four watersheds used for calibration in the study domain. However, only CFSR produced regional seasonal and long-term trends in water balance that compared favorably with independent Gravity Recovery and Climate Experiment (GRACE) and airborne altimetry data. Mean annual runoff using CFSR was 760 km3 yr-1, 8% of which was derived from the long-term removal of stored water from glaciers (glacier volume loss). The annual runoff from CFSR was partitioned into 63% snowmelt, 17% glacier ice melt, and 20% rainfall. Glacier runoff, taken as the sum of rainfall, snow, and ice melt occurring each season on glacier surfaces, was 38% of the total seasonal runoff, with the remaining runoff sourced from nonglacier surfaces. Our simulations suggests that existing GRACE solutions, previously reported to represent glacier mass balance alone, are actually measuring the full water budget of land and ice surfaces.

Domain size sensitivities of landfalling eastern Pacific atmospheric rivers

NASA Astrophysics Data System (ADS)

McClenny, E. E.; Ullrich, P. A.; Grotjahn, R.; Guan, B.; Waliser, D. E.

2017-12-01

Atmospheric rivers (ARs) concentrate a majority of mid-latitude latent heat transport into narrow bands. ARs making landfall along the North American coast typically originate in the waters surrounding Hawaii. We explore here the effects of explicitly simulating this "genesis region" on AR characteristics. We do this using two models and three domains centered on the North American coast. The Weather Research and Forecast (WRF) model, forced by National Center for Environmental Prediction Final Reanalysis data, provides a representative regional model. The simulation domains include: 1. Just off the coastline (100-130W), 2. The coastline to the Pacific just east of Hawaii (100-155W), and 3. The coastline to the Pacific west of Hawaii (100-180W). The Variable Resolution Community Earth System Model simulates ARs while preserving global interactions. In this global model, "domain" refers to the mesh refinement region, each of which corresponds to one of the three previously described WRF domains. We compare ARs from the wet season (October-April) for water years 2009-2017 in the test models against those found in the Modern Era Retrospective Reanalysis 2 (MERRA2). We objectively detect events with the global AR detection algorithm introduced in Guan and Waliser (2015). Comparisons between all model configurations and the reference MERRA2 data will be assessed by characteristics including landfall location (meridional distributions, including quartile ranges and standard deviations of landfalls across the coast), as well as vapor flux and precipitation (in terms of both the contribution of ARs to the larger regional climatology and any differences in the intensity of individual AR events across runs).

Surface wave effects in the NEMO ocean model: Forced and coupled experiments

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Mogensen, Kristian; Bidlot, Jean-Raymond; Balmaseda, Magdalena Alonso; Janssen, Peter A. E. M.

2015-04-01

The NEMO general circulation ocean model is extended to incorporate three physical processes related to ocean surface waves, namely the surface stress (modified by growth and dissipation of the oceanic wavefield), the turbulent kinetic energy flux from breaking waves, and the Stokes-Coriolis force. Experiments are done with NEMO in ocean-only (forced) mode and coupled to the ECMWF atmospheric and wave models. Ocean-only integrations are forced with fields from the ERA-Interim reanalysis. All three effects are noticeable in the extratropics, but the sea-state-dependent turbulent kinetic energy flux yields by far the largest difference. This is partly because the control run has too vigorous deep mixing due to an empirical mixing term in NEMO. We investigate the relation between this ad hoc mixing and Langmuir turbulence and find that it is much more effective than the Langmuir parameterization used in NEMO. The biases in sea surface temperature as well as subsurface temperature are reduced, and the total ocean heat content exhibits a trend closer to that observed in a recent ocean reanalysis (ORAS4) when wave effects are included. Seasonal integrations of the coupled atmosphere-wave-ocean model consisting of NEMO, the wave model ECWAM, and the atmospheric model of ECMWF similarly show that the sea surface temperature biases are greatly reduced when the mixing is controlled by the sea state and properly weighted by the thickness of the uppermost level of the ocean model. These wave-related physical processes were recently implemented in the operational coupled ensemble forecast system of ECMWF.

Dynamical downscaling with WRF for the Middle-East and North Africa

NASA Astrophysics Data System (ADS)

Dezfuli, A. K.; Zaitchik, B. F.; Badr, H. S.; Bergaoui, K.; Zaaboul, R.; Bhattacharjee, P.

2014-12-01

The Middle-East and North Africa (MENA) experience the highest risk of water stress in the world. This underlines the importance of climate analysis for water resources management and climate change adaptation for this region, particularly in transboundary basins such as the Tigris-Euphrates system. Such analysis, however, is difficult due to a paucity of high quality precipitation data. The network of gauge stations is quite sparse and the data are often available only at monthly time-scale. Satellite-based products, such as the Tropical Rainfall Measuring Mission (TRMM), offer better temporal resolution; however, these data are available only for periods that are short for hydroclimatic analysis, and they often misrepresent precipitation over regions with complex topography or strong convection. To fill this gap, we have implemented the Weather Research and Forecasting (WRF) Model, initialized with the NCEP/NCAR Reanalysis II, to generate high-resolution precipitation estimates for MENA. Several sensitivity analyses have been performed in order to find a set of physics parameters that appropriately captures the annual cycle and year-to-year variability of rainfall over select areas in MENA. The results show that WRF, particularly over highlands, estimates the precipitation more accurately than the satellite products. In addition to these reanalysis-driven simulations, we have performed several simulations using the historical and twenty first century outputs of a select number of GCMs available at the CMIP5 archive. These runs enable us to detect changes in rainfall behavior under different greenhouse gas scenarios and reveal synoptic to mesoscale mechanisms responsible for such changes.

Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite

NASA Astrophysics Data System (ADS)

Chen, Biyan; Liu, Zhizhao

2016-10-01

The variability and trend in global precipitable water vapor (PWV) from 1979 to 2014 are analyzed using the PWV data sets from the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis of the National Centers for Environmental Prediction (NCEP), radiosonde, Global Positioning System (GPS), and microwave satellite observations. PWV data from the ECMWF and NCEP have been evaluated by radiosonde, GPS, and microwave satellite observations, showing that ECMWF has higher accuracy than NCEP. Over the oceans, ECMWF has a much better agreement with the microwave satellite than NCEP. An upward trend in the global PWV is evident in all the five PWV data sets over three study periods: 1979-2014, 1992-2014, and 2000-2014. Positive global PWV trends, defined as percentage normalized by annual average, of 0.61 ± 0.33% decade-1, 0.57 ± 0.28% decade-1, and 0.17 ± 0.35% decade-1, have been derived from the NCEP, radiosonde, and ECMWF, respectively, for the period 1979-2014. It is found that ECMWF overestimates the PWV over the ocean prior to 1992. Thus, two more periods, 1992-2014 and 2000-2014, are studied. Increasing PWV trends are observed from all the five data sets in the two periods: 1992-2014 and 2000-2014. The linear relationship between PWV and surface temperature is positive over most oceans and the polar region. Steep positive/negative regression slopes are generally found in regions where large regional moisture flux divergence/convergence occurs.

An evaluation of the variable-resolution CESM for modeling California's climate: Evaluation of VR-CESM for Modeling California's Climate

DOE PAGES

Huang, Xingying; Rhoades, Alan M.; Ullrich, Paul A.; ...

2016-03-01

In this paper, the recently developed variable-resolution option within the Community Earth System Model (VR-CESM) is assessed for long-term regional climate modeling of California at 0.25° (~ 28 km) and 0.125° (~ 14 km) horizontal resolutions. The mean climatology of near-surface temperature and precipitation is analyzed and contrasted with reanalysis, gridded observational data sets, and a traditional regional climate model (RCM)—the Weather Research and Forecasting (WRF) model. Statistical metrics for model evaluation and tests for differential significance have been extensively applied. VR-CESM tended to produce a warmer summer (by about 1–3°C) and overestimated overall winter precipitation (about 25%–35%) compared tomore » reference data sets when sea surface temperatures were prescribed. Increasing resolution from 0.25° to 0.125° did not produce a statistically significant improvement in the model results. By comparison, the analogous WRF climatology (constrained laterally and at the sea surface by ERA-Interim reanalysis) was ~1–3°C colder than the reference data sets, underestimated precipitation by ~20%–30% at 27 km resolution, and overestimated precipitation by ~ 65–85% at 9 km. Overall, VR-CESM produced comparable statistical biases to WRF in key climatological quantities. Moreover, this assessment highlights the value of variable-resolution global climate models (VRGCMs) in capturing fine-scale atmospheric processes, projecting future regional climate, and addressing the computational expense of uniform-resolution global climate models.« less

Multi-year predictability of climate, drought, and wildfire in southwestern North America.

PubMed

Chikamoto, Yoshimitsu; Timmermann, Axel; Widlansky, Matthew J; Balmaseda, Magdalena A; Stott, Lowell

2017-07-26

Past severe droughts over North America have led to massive water shortages and increases in wildfire frequency. Triggering sources for multi-year droughts in this region include randomly occurring atmospheric blocking patterns, ocean impacts on atmospheric circulation, and climate's response to anthropogenic radiative forcings. A combination of these sources translates into a difficulty to predict the onset and length of such droughts on multi-year timescales. Here we present results from a new multi-year dynamical prediction system that exhibits a high degree of skill in forecasting wildfire probabilities and drought for 10-23 and 10-45 months lead time, which extends far beyond the current seasonal prediction activities for southwestern North America. Using a state-of-the-art earth system model along with 3-dimensional ocean data assimilation and by prescribing the external radiative forcings, this system simulates the observed low-frequency variability of precipitation, soil water, and wildfire probabilities in close agreement with observational records and reanalysis data. The underlying source of multi-year predictability can be traced back to variations of the Atlantic/Pacific sea surface temperature gradient, external radiative forcings, and the low-pass filtering characteristics of soils.

Approximate Stokes Drift Profiles and their use in Ocean Modelling

NASA Astrophysics Data System (ADS)

Breivik, O.; Biblot, J.; Janssen, P. A. E. M.

2016-02-01

Deep-water approximations to the Stokes drift velocity profile are explored as alternatives to the monochromatic profile. The alternative profiles investigated rely on the same two quantities required for the monochromatic profile, viz the Stokes transport and the surface Stokes drift velocity. Comparisons with parametric spectra and profiles under wave spectra from the ERA-Interim reanalysis and buoy observations reveal much better agreement than the monochromatic profile even for complex sea states. That the profiles give a closer match and a more correct shear has implications for ocean circulation models since the Coriolis-Stokes force depends on the magnitude and direction of the Stokes drift profile and Langmuir turbulence parameterizations depend sensitively on the shear of the profile. The NEMO general circulation ocean model was recently extended to incorporate the Stokes-Coriolis force along with two other wave-related effects. I will show some results from the coupled atmosphere-wave-ocean ensemble forecast system of ECMWF where these wave effects are now included in the ocean model component.

Seasonal forecasting of high wind speeds over Western Europe

NASA Astrophysics Data System (ADS)

Palutikof, J. P.; Holt, T.

2003-04-01

As financial losses associated with extreme weather events escalate, there is interest from end users in the forestry and insurance industries, for example, in the development of seasonal forecasting models with a long lead time. This study uses exceedences of the 90th, 95th, and 99th percentiles of daily maximum wind speed over the period 1958 to present to derive predictands of winter wind extremes. The source data is the 6-hourly NCEP Reanalysis gridded surface wind field. Predictor variables include principal components of Atlantic sea surface temperature and several indices of climate variability, including the NAO and SOI. Lead times of up to a year are considered, in monthly increments. Three regression techniques are evaluated; multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS). PCR and PLS proved considerably superior to MLR with much lower standard errors. PLS was chosen to formulate the predictive model since it offers more flexibility in experimental design and gave slightly better results than PCR. The results indicate that winter windiness can be predicted with considerable skill one year ahead for much of coastal Europe, but that this deteriorates rapidly in the hinterland. The experiment succeeded in highlighting PLS as a very useful method for developing more precise forecasting models, and in identifying areas of high predictability.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Variations of heat transport in the northwestern Pacific marginal seas inferred from high-resolution reanalysis

NASA Astrophysics Data System (ADS)

Seo, Gwang-Ho; Cho, Yang-Ki; Choi, Byoung-Ju

2014-02-01

High-resolution reanalysis of heat transport in the northwestern Pacific marginal seas was conducted for the period January 1980-December 2009 using ensemble Kalman filter. An ocean circulation model with a grid of 0.1 × 0.1° horizontal resolution and 20 vertical levels was used. Atmospheric forcing data from daily European Centre for Medium-Range Weather Forecasts were used in the ocean model. The assimilated data for the reanalysis were based on available observations of hydrographic profiles, including field surveys and Argo float and satellite-observed sea-surface temperature data. This study focused on mean and temporal variations in oceanic heat transport within the major straits among the marginal seas over 30 years. The mean heat transport in the Korea/Tsushima Strait and onshore transport across the shelf break in the East China Sea (ECS), Taiwan Strait, Tsugaru Strait, and Soya Strait were 182, 123, 82, 100, and 34 × 1012 W, respectively. The long-term trends in heat transport through the Korea/Tsushima Strait and Tsugaru Strait and onshore transport across the shelf break of the ECS were increasing, whereas the trend in heat transport through the Taiwan Strait was decreasing. There was little long-term change in heat transport in the Soya Strait. These long-term changes in heat transport through the Korea/Tsushima Strait, across the shelf of the ECS, and through the Taiwan Strait may be related to increased northeasterly wind stress in the ECS, which drives Ekman transport onto the shelf across the shelf break.

Evaluating the Large-Scale Environment of Extreme Events Using Reanalyses

NASA Astrophysics Data System (ADS)

Bosilovich, M. G.; Schubert, S. D.; Koster, R. D.; da Silva, A. M., Jr.; Eichmann, A.

2014-12-01

Extreme conditions and events have always been a long standing concern in weather forecasting and national security. While some evidence indicates extreme weather will increase in global change scenarios, extremes are often related to the large scale atmospheric circulation, but also occurring infrequently. Reanalyses assimilate substantial amounts of weather data and a primary strength of reanalysis data is the representation of the large-scale atmospheric environment. In this effort, we link the occurrences of extreme events or climate indicators to the underlying regional and global weather patterns. Now, with greater than 3o years of data, reanalyses can include multiple cases of extreme events, and thereby identify commonality among the weather to better characterize the large-scale to global environment linked to the indicator or extreme event. Since these features are certainly regionally dependent, and also, the indicators of climate are continually being developed, we outline various methods to analyze the reanalysis data and the development of tools to support regional evaluation of the data. Here, we provide some examples of both individual case studies and composite studies of similar events. For example, we will compare the large scale environment for Northeastern US extreme precipitation with that of highest mean precipitation seasons. Likewise, southerly winds can shown to be a major contributor to very warm days in the Northeast winter. While most of our development has involved NASA's MERRA reanalysis, we are also looking forward to MERRA-2 which includes several new features that greatly improve the representation of weather and climate, especially for the regions and sectors involved in the National Climate Assessment.

Nonhydrostatic nested climate modeling: A case study of the 2010 summer season over the western United States

NASA Astrophysics Data System (ADS)

Lebassi-Habtezion, Bereket; Diffenbaugh, Noah S.

2013-10-01

potential importance of local-scale climate phenomena motivates development of approaches to enable computationally feasible nonhydrostatic climate simulations. To that end, we evaluate the potential viability of nested nonhydrostatic model approaches, using the summer climate of the western United States (WUSA) as a case study. We use the Weather Research and Forecast (WRF) model to carry out five simulations of summer 2010. This suite allows us to test differences between nonhydrostatic and hydrostatic resolutions, single and multiple nesting approaches, and high- and low-resolution reanalysis boundary conditions. WRF simulations were evaluated against station observations, gridded observations, and reanalysis data over domains that cover the 11 WUSA states at nonhydrostatic grid spacing of 4 km and hydrostatic grid spacing of 25 km and 50 km. Results show that the nonhydrostatic simulations more accurately resolve the heterogeneity of surface temperature, precipitation, and wind speed features associated with the topography and orography of the WUSA region. In addition, we find that the simulation in which the nonhydrostatic grid is nested directly within the regional reanalysis exhibits the greatest overall agreement with observational data. Results therefore indicate that further development of nonhydrostatic nesting approaches is likely to yield important insights into the response of local-scale climate phenomena to increases in global greenhouse gas concentrations. However, the biases in regional precipitation, atmospheric circulation, and moisture flux identified in a subset of the nonhydrostatic simulations suggest that alternative nonhydrostatic modeling approaches such as superparameterization and variable-resolution global nonhydrostatic modeling will provide important complements to the nested approaches tested here.

Evaluation of Extratropical Cyclone Precipitation in the North Atlantic Basin: An analysis of ERA-Interim, WRF, and two CMIP5 models.

PubMed

Booth, James F; Naud, Catherine M; Willison, Jeff

2018-03-01

The representation of extratropical cyclones (ETCs) precipitation in general circulation models (GCMs) and a weather research and forecasting (WRF) model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic Basin. Lagrangian cyclone tracks of ETCs in ERA-Interim reanalysis (ERAI), the GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km resolution WRF model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with GISS generating the most, followed by ERAI and then GFDL. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

A Cause and A Solution for the Underprediction of Extreme Wave Events in the Northeast Pacific

NASA Astrophysics Data System (ADS)

Ellenson, A. N.; Ozkan-Haller, H. T.; Thomson, J.; Brown, A. C.; Haller, M. C.

2016-12-01

Along the coastlines of Washington and Oregon, at least one 10 m wave height event occurs every year, and the strongest storms produce wave heights of 14-15 m. Extremely high wave heights can cause severe damage to coastal infrastructure and pose hazards to stakeholders along the coast. A system which can accurately predict such sea states is important for quantifying risk and aiding in preparation for extreme wave events. This study explores how to optimize forecast model performance for extreme wave events by utilizing different physics packages or wind input in four model configurations. The different wind input products consist of a reanalyzed Global Forecasting System (GFS) wind input and a Climate Forecast System Reanalysis (CFSR) from the National Center of Environmental Prediction (NCEP). The physics packages are the Tolman-Chalikov (1996) ST2 physics package and the Ardhuin et al (2009) ST4 physics package associated with version 4.18 of WaveWatch III. A hindcast was previously performed to assess the wave character along the Pacific Northwest Coastline for wave energy applications. Inspection of hindcast model results showed that the operational model, which consisted of ST2 physics and GFS wind, underpredicted events where wave height exceeded six meters.The under-prediction is most severe for cases with the combined conditions of a distant cyclone and a strong coastal jet. Three such cases were re-analyzed with the four model configurations. Model output is compared with observations at NDBC buoy 46050, offshore of Newport, OR. The model configuration consisting of ST4 physics package and CFSR wind input performs best as compared with the original model, reducing significant wave height underprediction from 1.25 m to approximately 0.67 m and mean wave direction error from 30 degrees to 17 degrees for wave heights greater than 6 m. Spectral analysis shows that the ST4-CFSR model configuration best resolves southerly wave energy, and all model configurations tend to overestimate northerly wave energy. This directional distinction is important when attempting to identify which atmospheric feature has induced the extreme wave energy.

An operational system for the assimilation of satellite information on wild-land fires for the needs of air quality modelling and forecasting

NASA Astrophysics Data System (ADS)

Sofiev, M.; Vankevich, R.; Lanne, M.; Prank, M.; Petukhov, V.; Ermakova, T.; Kukkonen, J.

2009-03-01

This paper investigates a potential of two remotely sensed wild-land fire characteristics: 4-μm Brightness Temperature Anomaly (TA) and Fire Radiative Power (FRP) for the needs of operational chemical transport modelling and the short-term forecasting of the atmospheric composition and air quality. Two treatments of the TA and FRP data are presented and a methodology for evaluating the emission fluxes is described. The method does not contain a complicated analysis of vegetation state, fuel load, burning efficiency and related factors, which are comparatively uncertain but inevitably involved in approaches based on burnt-area scars or similar products. The core of the current methodology is based on the empirical emission factors that have been derived from the analysis of several fire episodes in Europe (28 April-5 May 2006, 15-25 August 2006, August 2008 etc.). These episodes were characterised by: (i) well-identified FRP and TA values, and (ii) available independent observations of aerosol concentrations and optical thickness for the regions where fire smoke was dominant in comparison with contributions of other pollution sources. The emission factors were determined separately for the forested and grassland areas; in case of mixed-type land use an intermediate scaling was assumed. Despite significant difference between the TA and FRP products, an accurate non-linear fitting between the approaches was found. The agreement was comparatively weak only for small fires where the accuracy of both products is low. The re-analysis and forecasting applications of the Fire Assimilation System (FAS) showed that both TA and FRP products are suitable for evaluation of the emission fluxes from the wild-land fires. The concentrations of aerosols predicted by the regional dispersion modelling system SILAM appear within a factor of 2-3 from observations. The main areas of improvement include further refining the emission factors over the globe, explicit determination and appropriate treatment of the type of fires, evaluation of the injection height of the plumes and predicting the fire temporal evolution.

Quasi-biennial modulation of the Northern Hemisphere tropopause height and temperature

NASA Astrophysics Data System (ADS)

Ribera, P.; PeñA-Ortiz, C.; AñEl, J. A.; Gimeno, L.; de la Torre, L.; Gallego, D.

2008-04-01

The influence of the quasi-biennial oscillation (QBO) on the tropopause pressure and temperature is studied through the application of the multitaper-singular value decomposition method (MTM-SVD). Reanalysis data (ERA-40) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and radiosonde data from the Integrated Global Radiosonde Archive (IGRA) covering the period 1979-1999 are used. The results show a strong response of the height and temperature of the tropopause to the QBO not limited to the equatorial latitudes but affecting the entire Northern Hemisphere. A cooling (warming) of the tropopause temperature over polar (equatorial) latitudes during a QBO positive phase is observed, being particularly noticeable over polar latitudes. The anomalies in the tropopause height confirm these results, with the tropopause being at higher (lower) levels in polar (equatorial) latitudes during QBO positive phase. Results for the QBO negative phase are of opposite sign. We also found that the results obtained using raw radiosonde data and reanalysis are in very good agreement. Finally, the evolution of the mass stream function through a QBO cycle is used to justify the differences observed in the evolution of the tropopause characteristics at low and high latitudes through the QBO cycle.

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2004-01-01

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Global 3-D ionospheric electron density reanalysis based on multisource data assimilation

NASA Astrophysics Data System (ADS)

Yue, Xinan; Schreiner, William S.; Kuo, Ying-Hwa; Hunt, Douglas C.; Wang, Wenbin; Solomon, Stanley C.; Burns, Alan G.; Bilitza, Dieter; Liu, Jann-Yenq; Wan, Weixing; Wickert, Jens

2012-09-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis demonstration study during 2002-2011 based on multisource data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground-based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 h in universal time, 5° in latitude, 10° in longitude, and ˜30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content derived from the international GNSS service global ionospheric maps, the observed electron density profiles from the Poker Flat Incoherent Scatter Radar during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Extending the reanalysis to the ionosphere based on ground and LEO based GNSS observations

NASA Astrophysics Data System (ADS)

Yue, X.; Schreiner, W. S.; Kuo, Y.

2012-12-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis during 2002-2011 based on multi-source data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 hr in universal time, 5o in latitude, 10o in longitude, and ~ 30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content (GEC) derived from the international GNSS service (IGS) global ionospheric maps (GIM), the observed electron density profiles from the Poker Flat Incoherent Scatter Radar (PFISR) during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F-region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

NCAR's Research Data Archive: OPeNDAP Access for Complex Datasets

NASA Astrophysics Data System (ADS)

Dattore, R.; Worley, S. J.

2014-12-01

Many datasets have complex structures including hundreds of parameters and numerous vertical levels, grid resolutions, and temporal products. Making these data accessible is a challenge for a data provider. OPeNDAP is powerful protocol for delivering in real-time multi-file datasets that can be ingested by many analysis and visualization tools, but for these datasets there are too many choices about how to aggregate. Simple aggregation schemes can fail to support, or at least make it very challenging, for many potential studies based on complex datasets. We address this issue by using a rich file content metadata collection to create a real-time customized OPeNDAP service to match the full suite of access possibilities for complex datasets. The Climate Forecast System Reanalysis (CFSR) and it's extension, the Climate Forecast System Version 2 (CFSv2) datasets produced by the National Centers for Environmental Prediction (NCEP) and hosted by the Research Data Archive (RDA) at the Computational and Information Systems Laboratory (CISL) at NCAR are examples of complex datasets that are difficult to aggregate with existing data server software. CFSR and CFSv2 contain 141 distinct parameters on 152 vertical levels, six grid resolutions and 36 products (analyses, n-hour forecasts, multi-hour averages, etc.) where not all parameter/level combinations are available at all grid resolution/product combinations. These data are archived in the RDA with the data structure provided by the producer; no additional re-organization or aggregation have been applied. Since 2011, users have been able to request customized subsets (e.g. - temporal, parameter, spatial) from the CFSR/CFSv2, which are processed in delayed-mode and then downloaded to a user's system. Until now, the complexity has made it difficult to provide real-time OPeNDAP access to the data. We have developed a service that leverages the already-existing subsetting interface and allows users to create a virtual dataset with its own structure (das, dds). The user receives a URL to the customized dataset that can be used by existing tools to ingest, analyze, and visualize the data. This presentation will detail the metadata system and OPeNDAP server that enable user-customized real-time access and show an example of how a visualization tool can access the data.

Simulation of boreal Summer Monsoon Rainfall using CFSV2_SSiB model: sensitivity to Land Use Land Cover (LULC)

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.

2016-12-01

The land surface play a vital role in determining the surface energy budget, accurate representation of land use and land cover (LULC) is necessary to improve forecast. In this study, we have investigated the influence of surface vegetation maps with different LULC on simulating the boreal summer monsoon rainfall. Using a National Centres for Environmental Prediction (NCEP) Coupled Forecast System version 2(CFSv2) model coupled with Simplified Simple Biosphere (SSiB) model, two experiments were conducted: one with old vegetation map and one with new vegetation map. The significant differences between new and old vegetation map were in semi-arid and arid areas. For example, in old map Tibetan plateau classified as desert, which is not appropriate, while in new map it was classified as grasslands or shrubs with bare soil. Old map classified the Sahara desert as a bare soil and shrubs with bare soil, whereas in new map it was classified as bare ground. In addition to central Asia and the Sahara desert, in new vegetation map, Europe had more cropped area and India's vegetation cover was changed from crops and forests to wooded grassland and small areas of grassland and shrubs. The simulated surface air temperature with new map shows a significant improvement over Asia, South Africa, and northern America by some 1 to 2ºC and 2 to 3ºC over north east China and these are consistent with the reduced rainfall biases over Africa, near Somali coast, north east India, Bangladesh, east China sea, eastern Pacific and northern USA. Over Indian continent and bay of Bengal dry rainfall anomalies that is the only area showing large dry rainfall bias, however, they were unchanged with new map simulation. Overall the CFSv2(coupled with SSiB) model with new vegetation map show a promising result in improving the monsoon forecast by improving the Land -Atmosphere interactions. To compare with the LULC forcing, experiment was conducted using the Global Forecast System (GFS) simulations forced with different observed Sea Surface Temperatures (SST) for the same period: one is from NCEP reanalysis and one from Hadley Center. They have substantial difference in Indian Ocean. Preliminary analysis shows that, the impact of these two SST data sets on Indian summer monsoon rainfall has no significant impact.

Evaluating the Impact of the Summit Station, Greenland Radiosonde Program on Science and Forecast Services

NASA Astrophysics Data System (ADS)

Martinez, C. J.; Starkweather, S.; Cox, C. J.; Solomon, A.; Shupe, M.

2015-12-01

Radiosondes are balloon-borne meteorological sensors used to acquire profiles of temperature and humidity. Radiosonde data are essential inputs for numerical weather prediction models and are used for climate research, particularly in the creation of reanalysis products. However, radiosonde programs are costly to maintain, in particular in the remote regions of the Arctic (e.g., $440,000/yr at Summit, Greenland), where only 40 of approximately 1000 routine global launches are made. The climate of this data-sparse region is poorly understood and forecast data assimilation procedures are designed for global applications. Thus, observations may be rejected from the data assimilation because they are too far from the model expectations. For the most cost-efficient deployment of resources and to improve forecasting methods, analyses of the effectiveness of individual radiosonde programs are necessary. Here, we evaluate how radiosondes launched twice daily (0 and 12 UTC) from Summit Station, Greenland, (72.58⁰N, 38.48⁰W, 3210 masl) influence the European Centre for Medium Range Weather Forecasting (ECMWF) operational forecasts from June 2013 through May of 2015. A statistical analysis is conducted to determine the impact of the observations on the forecast model and the meteorological regimes that the model fails to reproduce are identified. Assimilation rates in the inversion layer are lower than any other part of the troposphere. Above the inversion, assimilation rates range from 85%-100%, 60%-98%, and > 99% for temperature, humidity, and wind, respectively. The lowest assimilation rates are found near the surface, possibly associated with biases in the representation of the temperature inversion by the ECMWF model at Summit. Consequently, assimilation rates are lower near the surface during winter when strong temperature inversions are frequently observed. Our findings benefit the scientific community who uses this information for climatological analysis of the Greenland Ice Sheet, and thus further analysis is warranted.

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

USGS Publications Warehouse

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

Toward a chemical reanalysis in a coupled chemistry-climate model: An evaluation of MOPITT CO assimilation and its impact on tropospheric composition

NASA Astrophysics Data System (ADS)

Gaubert, B.; Arellano, A. F.; Barré, J.; Worden, H. M.; Emmons, L. K.; Tilmes, S.; Buchholz, R. R.; Vitt, F.; Raeder, K.; Collins, N.; Anderson, J. L.; Wiedinmyer, C.; Martinez Alonso, S.; Edwards, D. P.; Andreae, M. O.; Hannigan, J. W.; Petri, C.; Strong, K.; Jones, N.

2016-06-01

We examine in detail a 1 year global reanalysis of carbon monoxide (CO) that is based on joint assimilation of conventional meteorological observations and Measurement of Pollution in The Troposphere (MOPITT) multispectral CO retrievals in the Community Earth System Model (CESM). Our focus is to assess the impact to the chemical system when CO distribution is constrained in a coupled full chemistry-climate model like CESM. To do this, we first evaluate the joint reanalysis (MOPITT Reanalysis) against four sets of independent observations and compare its performance against a reanalysis with no MOPITT assimilation (Control Run). We then investigate the CO burden and chemical response with the aid of tagged sectoral CO tracers. We estimate the total tropospheric CO burden in 2002 (from ensemble mean and spread) to be 371 ± 12% Tg for MOPITT Reanalysis and 291 ± 9% Tg for Control Run. Our multispecies analysis of this difference suggests that (a) direct emissions of CO and hydrocarbons are too low in the inventory used in this study and (b) chemical oxidation, transport, and deposition processes are not accurately and consistently represented in the model. Increases in CO led to net reduction of OH and subsequent longer lifetime of CH4 (Control Run: 8.7 years versus MOPITT Reanalysis: 9.3 years). Yet at the same time, this increase led to 5-10% enhancement of Northern Hemisphere O3 and overall photochemical activity via HOx recycling. Such nonlinear effects further complicate the attribution to uncertainties in direct emissions alone. This has implications to chemistry-climate modeling and inversion studies of longer-lived species.

Simulation of Changes in the Near-Surface Soil Freeze/Thaw Cycle Using CLM4.5 With Four Atmospheric Forcing Data Sets

NASA Astrophysics Data System (ADS)

Guo, Donglin; Wang, Aihui; Li, Duo; Hua, Wei

2018-03-01

Change in the near-surface soil freeze/thaw cycle is critical for assessments of hydrological activity, ecosystems, and climate change. Previous studies investigated the near-surface soil freeze/thaw cycle change mostly based on in situ observations and satellite monitoring. Here numerical simulation method is tested to estimate the long-term change in the near-surface soil freeze/thaw cycle in response to recent climate warming for its application to predictions. Four simulations are performed at 0.5° × 0.5° resolution from 1979 to 2009 using the Community Land Model version 4.5, each driven by one of the four atmospheric forcing data sets (i.e., one default Climate Research Unit-National Centers for Environmental Prediction [CRUNCEP] and three newly developed Modern Era Retrospective-Analysis for Research and Applications, Climate Forecast System Reanalysis, and European Centre for Medium-Range Weather Forecasts Reanalysis Interim). The observations from 299 weather stations in both Russia and China are employed to validate the simulated results. The results show that all simulations reasonably reproduce the observed variations in the ground temperature, the freeze start and end dates, and the freeze duration (the correlation coefficients range from 0.47 to 0.99, and the Nash-Sutcliffe efficiencies range from 0.19 to 0.98). Part of the simulations also exactly simulate the trends of the ground temperature, the freeze start and end dates, and the freeze duration. Of the four simulations, the results from the simulation using the CRUNCEP data set show the best overall agreement with the in situ observations, indicating that the CRUNCEP data set could be preferentially considered as the basic atmospheric forcing data set for future prediction. The simulated area-averaged annual freeze duration shortened by 8.03 days on average from 1979 to 2009, with an uncertainty (one standard deviation) of 0.67 days caused by the different atmospheric forcing data sets. These results address the performance of numerical model in simulating the long-term changes in the near-surface soil freeze/thaw cycle and the role of different atmospheric forcing data sets in the simulation, which are useful for the prediction of future freeze/thaw dynamics.

Propagation Route and Speed of Swell in the Indian Ocean

NASA Astrophysics Data System (ADS)

Zheng, C. W.; Li, C. Y.; Pan, J.

2018-01-01

The characteristics of swell propagation play an important role in the forecasting of ocean waves as well as on research on global climate change, wave energy development, and disaster prevention and reduction. To reveal the propagation routes, terminal targets and speeds of swells that originate from the southern Indian Ocean westerly (SIOW), an intraseasonal swell index (SI) was defined based on the 45 year (September 1957 to August 2002) ERA-40 wave reanalysis data product from the European Center for Medium-Range Weather Forecasts (ECMWF). The results show that the main body of the SIOW-related swells typically spread to the waters off Sri Lanka and Christmas Island, while the branches spread to the Arabian Sea and other waters. The propagation speeds of swells originated in the SIOW were fastest in May and August, followed by November, and were slowest in February. Swells usually required 4-6 days to propagate from the western part of the SIOW to the waters off Sri Lanka and Christmas Island, whereas swells usually required 2-4 days to propagate from the eastern part of the SIOW to the waters off Christmas Island.

The roles of static stability and tropical-extratropical interactions in the summer interannual variability of the North Atlantic sector

NASA Astrophysics Data System (ADS)

Mbengue, Cheikh Oumar; Woollings, Tim; Dacre, Helen F.; Hodges, Kevin I.

2018-04-01

Summer seasonal forecast skill in the North Atlantic sector is lower than winter skill. To identify potential controls on predictability, the sensitivity of North Atlantic baroclinicity to atmospheric drivers is quantified. Using ERA-INTERIM reanalysis data, North Atlantic storm-track baroclinicity is shown to be less sensitive to meridional temperature-gradient variability in summer. Static stability shapes the sector's interannual variability by modulating the sensitivity of baroclinicity to variations in meridional temperature gradients and tropopause height and by modifying the baroclinicity itself. High static stability anomalies at upper levels result in more zonal extratropical cyclone tracks and higher eddy kinetic energy over the British Isles in the summertime. These static stability anomalies are not strongly related to the summer NAO; but they are correlated with the suppression of convection over the tropical Atlantic and with a poleward-shifted subtropical jet. These results suggest a non-local driver of North Atlantic variability. Furthermore, they imply that improved representations of convection over the south-eastern part of North America and the tropical Atlantic might improve summer seasonal forecast skill.

Weakening of Indian Summer Monsoon Rainfall due to Changes in Land Use Land Cover

PubMed Central

Paul, Supantha; Ghosh, Subimal; Oglesby, Robert; Pathak, Amey; Chandrasekharan, Anita; Ramsankaran, RAAJ

2016-01-01

Weakening of Indian summer monsoon rainfall (ISMR) is traditionally linked with large-scale perturbations and circulations. However, the impacts of local changes in land use and land cover (LULC) on ISMR have yet to be explored. Here, we analyzed this topic using the regional Weather Research and Forecasting model with European Center for Medium range Weather Forecast (ECMWF) reanalysis data for the years 2000–2010 as a boundary condition and with LULC data from 1987 and 2005. The differences in LULC between 1987 and 2005 showed deforestation with conversion of forest land to crop land, though the magnitude of such conversion is uncertain because of the coarse resolution of satellite images and use of differential sources and methods for data extraction. We performed a sensitivity analysis to understand the impacts of large-scale deforestation in India on monsoon precipitation and found such impacts are similar to the observed changes in terms of spatial patterns and magnitude. We found that deforestation results in weakening of the ISMR because of the decrease in evapotranspiration and subsequent decrease in the recycled component of precipitation. PMID:27553384

Understanding and Seasonal Forecasting of multiscale droughts in China

NASA Astrophysics Data System (ADS)

Yuan, X.; Wang, L.; Wang, S.; Zhang, M.

2016-12-01

Droughts were climate anomalies that occurred naturally. But they have been altered by climate change and human interventions, and have covered a variety of spatiotemporal scales from seasonal/decadal droughts at regional to continental scales that are associated with large-scale climate anomalies and certain atmospheric circulation patterns, to flash droughts at local scales that are usually concurrent with heat extremes. Droughts have quite different implications across a number of sectors, with the considerations augmented from meteorological droughts to agricultural and hydrological droughts, where the latter could be affected by human activities directly. This raises a grand challenge to understand and predict droughts across scales in a changing environment. This presentation will be started by diagnosing an El Niño-induced meteorological drought that occurred over northern China (NC) last year, where the oceanic and atmospheric background are investigated, and the real-time prediction from Climate Forecast System version 2 (CFSv2) are diagnosed. The comparison between 2015 NC drought and other historical droughts are discussed, and a dynamical-statistical forecasting approach is being developed. Secondly, a rapidly developing agricultural drought event that termed as "flash droughts" accompanied by extreme heat, low soil moisture and high evapotranspiration (ET), occurred frequently around the world, and caused devastating impacts on crop yields and water supply. The increasing trend of flash droughts over China was tripled after the big El Niño event in 1997/98, but the warming hiatus does exist over many regions of China. The changes in flash droughts over China are being attributed by using multiple reanalysis data and the CMIP5 simulations. Lastly, the effects of human interventions on the drought propagation will be investigated over Yellow River basin in northern China. A comparison between SPI and standardized streamflow index indicates that the response of hydrological droughts to meteorological droughts becomes longer, and the duration and severity of hydrological droughts could be doubled or tripled with human interventions. The impact of human intervention on the hydrological drought predictability is being explored within the NMME/VIC forecasting framework.

Extending Climate Analytics as a Service to the Earth System Grid Federation Progress Report on the Reanalysis Ensemble Service

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.

2016-12-01

We are extending climate analytics-as-a-service, including: (1) A high-performance Virtual Real-Time Analytics Testbed supporting six major reanalysis data sets using advanced technologies like the Cloudera Impala-based SQL and Hadoop-based MapReduce analytics over native NetCDF files. (2) A Reanalysis Ensemble Service (RES) that offers a basic set of commonly used operations over the reanalysis collections that are accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib. (3) An Open Geospatial Consortium (OGC) WPS-compliant Web service interface to CDSLib to accommodate ESGF's Web service endpoints. This presentation will report on the overall progress of this effort, with special attention to recent enhancements that have been made to the Reanalysis Ensemble Service, including the following: - An CDSlib Python library that supports full temporal, spatial, and grid-based resolution services - A new reanalysis collections reference model to enable operator design and implementation - An enhanced library of sample queries to demonstrate and develop use case scenarios - Extended operators that enable single- and multiple reanalysis area average, vertical average, re-gridding, and trend, climatology, and anomaly computations - Full support for the MERRA-2 reanalysis and the initial integration of two additional reanalyses - A prototype Jupyter notebook-based distribution mechanism that combines CDSlib documentation with interactive use case scenarios and personalized project management - Prototyped uncertainty quantification services that combine ensemble products with comparative observational products - Convenient, one-stop shopping for commonly used data products from multiple reanalyses, including basic subsetting and arithmetic operations over the data and extractions of trends, climatologies, and anomalies - The ability to compute and visualize multiple reanalysis intercomparisons

Assessment of surface air temperature over the Arctic Ocean in reanalysis and IPCC AR4 model simulations with IABP/POLES observations

NASA Astrophysics Data System (ADS)

Liu, Jiping; Zhang, Zhanhai; Hu, Yongyun; Chen, Liqi; Dai, Yongjiu; Ren, Xiaobo

2008-05-01

The surface air temperature (SAT) over the Arctic Ocean in reanalyses and global climate model simulations was assessed using the International Arctic Buoy Programme/Polar Exchange at the Sea Surface (IABP/POLES) observations for the period 1979-1999. The reanalyses, including the National Centers for Environmental Prediction Reanalysis II (NCEP2) and European Centre for Medium-Range Weather Forecast 40-year Reanalysis (ERA40), show encouraging agreement with the IABP/POLES observations, although some spatiotemporal discrepancies are noteworthy. The reanalyses have warm annual mean biases and underestimate the observed interannual SAT variability in summer. Additionally, NCEP2 shows an excessive warming trend. Most model simulations (coordinated by the International Panel on Climate Change for its Fourth Assessment Report) reproduce the annual mean, seasonal cycle, and trend of the observed SAT reasonably well, particularly the multi-model ensemble mean. However, large discrepancies are found. Some models have the annual mean SAT biases far exceeding the standard deviation of the observed interannul SAT variability and the across-model standard deviation. Spatially, the largest inter-model variance of the annual mean SAT is found over the North Pole, Greenland Sea, Barents Sea and Baffin Bay. Seasonally, a large spread of the simulated SAT among the models is found in winter. The models show interannual variability and decadal trend of various amplitudes, and can not capture the observed dominant SAT mode variability and cooling trend in winter. Further discussions of the possible attributions to the identified SAT errors for some models suggest that the model's performance in the sea ice simulation is an important factor.

Diagnosing Possible Anthropogenic Contributions to Heavy Colorado Rainfall in September 2013

NASA Astrophysics Data System (ADS)

Pall, Pardeep; Patricola, Christina; Wehner, Michael; Stone, Dáithí; Paciorek, Christopher; Collins, William

2015-04-01

Unusually heavy rainfall occurred over the Colorado Front Range during early September 2013, with record or near-record totals recorded in several locations. It was associated predominantly with a stationary large-scale weather pattern (akin to the North American Monsoon, which occurs earlier in the year) that drove a strong plume of deep moisture inland from the Gulf of Mexico against the Front Range foothills. The resulting floods across the South Platte River basin impacted several thousands of people and many homes, roads, and businesses. To diagnose possible anthropogenic contributions to the odds of such heavy rainfall, we adapt an existing event attribution paradigm of modelling an 'event that was' for September 2013 and comparing it to a modelled 'event that might have been' for that same time but for the absence of historical anthropogenic drivers of climate. Specifically, we first perform 'event that was' simulations with the regional Weather Research and Forecasting (WRF) model at 12 km resolution over North America, driven by NCEP2 re-analysis. We then re-simulate, having adjusted the re-analysis to 'event that might have been conditions' by modifying atmospheric greenhouse gas and other pollutant concentrations, temperature, humidity, and winds, as well as sea ice coverage, and sea-surface temperatures - all according to estimates from global climate model simulations. Thus our findings are highly conditional on the driving re-analysis and adjustments therein, but the setup allows us to elucidate possible mechanisms responsible for heavy Colorado rainfall in September 2013. Our model results suggests that, given an insignificant change in the pattern of large-scale driving weather, there is an increase in atmospheric water vapour under anthropogenic climate warming leading to a substantial increase in the probability of heavy rainfall occurring over the South Platte River basin in September 2013.

Effects of downscaled high-resolution meteorological data on the PSCF identification of emission sources

DOE PAGES

Cheng, Meng -Dawn; Kabela, Erik D.

2016-04-30

The Potential Source Contribution Function (PSCF) model has been successfully used for identifying regions of emission source at a long distance in this study, the PSCF model relies on backward trajectories calculated by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. In this study, we investigated the impacts of grid resolution and Planetary Boundary Layer (PBL) parameterization (e.g., turbulent transport of pollutants) on the PSCF analysis. The Mellor-Yamada-Janjic (MYJ) and Yonsei University (YUS) parameterization schemes were selected to model the turbulent transport in the PBL within the Weather Research and Forecasting (WRF version 3.6) model. Two separate domain grid sizesmore » (83 and 27 km) were chosen in the WRF downscaling in generating the wind data for driving the HYSPLIT calculation. The effects of grid size and PBL parameterization are important in incorporating the influ- ence of regional and local meteorological processes such as jet streaks, blocking patterns, Rossby waves, and terrain-induced convection on the transport of pollutants by a wind trajectory. We found high resolution PSCF did discover and locate source areas more precisely than that with lower resolution meteorological inputs. The lack of anticipated improvement could also be because a PBL scheme chosen to produce the WRF data was only a local parameterization and unable to faithfully duplicate the real atmosphere on a global scale. The MYJ scheme was able to replicate PSCF source identification by those using the Reanalysis and discover additional source areas that was not identified by the Reanalysis data. In conclusion, a potential benefit for using high-resolution wind data in the PSCF modeling is that it could discover new source location in addition to those identified by using the Reanalysis data input.« less

An 8-Year, High-Resolution Reanalysis of Atmospheric CO2 Mixing Ratios Based on OCO-2 and GOSAT-ACOS Retrievals

NASA Technical Reports Server (NTRS)

Weir, B.; Chatterjee, A.; Ott, L. E.; Pawson, S.

2017-01-01

The NASA GMAO (Global Modeling and Assimilation Office) reanalysis blends OCO-2 (Orbiting Carbon Observatory 2) and GOSAT-ACOS (Greenhouse Gases Observing Satellite-Atmospheric Carbon Observations from Space) retrievals (top) with GEOS (Goddard Earth Observing System) model predictions (bottom) to estimate the full 3D (three-dimensional) state of CO2 every 3 hours (middle). This poster describes monthly atmospheric growth rates derived from the reanalysis and an application to aircraft data with the potential to aid bias correction.

Investigating Mesoscale Convective Systems and their Predictability Using Machine Learning

NASA Astrophysics Data System (ADS)

Daher, H.; Duffy, D.; Bowen, M. K.

2016-12-01

A mesoscale convective system (MCS) is a thunderstorm region that lasts several hours long and forms near weather fronts and can often develop into tornadoes. Here we seek to answer the question of whether these tornadoes are "predictable" by looking for a defining characteristic(s) separating MCSs that evolve into tornadoes versus those that do not. Using NASA's Modern Era Retrospective-analysis for Research and Applications 2 reanalysis data (M2R12K), we apply several state of the art machine learning techniques to investigate this question. The spatial region examined in this experiment is Tornado Alley in the United States over the peak tornado months. A database containing select variables from M2R12K is created using PostgreSQL. This database is then analyzed using machine learning methods such as Symbolic Aggregate approXimation (SAX) and DBSCAN (an unsupervised density-based data clustering algorithm). The incentive behind using these methods is to mathematically define a MCS so that association rule mining techniques can be used to uncover some sort of signal or teleconnection that will help us forecast which MCSs will result in tornadoes and therefore give society more time to prepare and in turn reduce casualties and destruction.

Reliability of windstorm predictions in the ECMWF ensemble prediction system

NASA Astrophysics Data System (ADS)

Becker, Nico; Ulbrich, Uwe

2016-04-01

Windstorms caused by extratropical cyclones are one of the most dangerous natural hazards in the European region. Therefore, reliable predictions of such storm events are needed. Case studies have shown that ensemble prediction systems (EPS) are able to provide useful information about windstorms between two and five days prior to the event. In this work, ensemble predictions with the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS are evaluated in a four year period. Within the 50 ensemble members, which are initialized every 12 hours and are run for 10 days, windstorms are identified and tracked in time and space. By using a clustering approach, different predictions of the same storm are identified in the different ensemble members and compared to reanalysis data. The occurrence probability of the predicted storms is estimated by fitting a bivariate normal distribution to the storm track positions. Our results show, for example, that predicted storm clusters with occurrence probabilities of more than 50% have a matching observed storm in 80% of all cases at a lead time of two days. The predicted occurrence probabilities are reliable up to 3 days lead time. At longer lead times the occurrence probabilities are overestimated by the EPS.

The potential of air-sea interactions for improving summertime North Atlantic seasonal forecasts

NASA Astrophysics Data System (ADS)

Ossó, Albert; Shaffrey, Len; Dong, Buwen; Sutton, Rowan

2017-04-01

Delivering skillful summertime seasonal forecasts of the Northern Hemisphere (NH) mid-latitude climate is a key unresolved issue for the climate science community. Current climate models have some skill in forecasting the wintertime NH mid-latitude circulation but very limited skill during summertime. To explore the potential predictability of the summertime climate we analyze lagged correlation patterns between the SSTs and summer atmospheric circulation in the North Atlantic both in observations and climate model outputs. We find observational evidence in the ERA-Interim (1979-2015) reanalysis and the HadSLP2 and HadISST data of an SST pattern forced by late winter atmospheric circulation persisting from winter to early summer that excites an anticyclonic summer SLP anomaly west of the British Isles. We show that the atmospheric response is driven through the action of turbulent heat fluxes and changes on the background baroclinicity. The lagged atmospheric response to the SSTs could be exploited for summertime predictability over Western Europe. We find a statistical significant correlation of over 0.6 between April-May North Atlantic SSTs and the June-August North Atlantic SLP anomaly. The previous findings are further explored using 120 years of coupled ocean-atmosphere HadGEM3-GC2 model simulation. The climate model qualitatively reproduces the observed spatial relationship between the late winter and spring SSTs and summertime circulation, although the correlations are substantially weaker than observed.

Enhanced outage prediction modeling for strong extratropical storms and hurricanes in the Northeastern United States

NASA Astrophysics Data System (ADS)

Cerrai, D.; Anagnostou, E. N.; Wanik, D. W.; Bhuiyan, M. A. E.; Zhang, X.; Yang, J.; Astitha, M.; Frediani, M. E.; Schwartz, C. S.; Pardakhti, M.

2016-12-01

The overwhelming majority of human activities need reliable electric power. Severe weather events can cause power outages, resulting in substantial economic losses and a temporary worsening of living conditions. Accurate prediction of these events and the communication of forecasted impacts to the affected utilities is necessary for efficient emergency preparedness and mitigation. The University of Connecticut Outage Prediction Model (OPM) uses regression tree models, high-resolution weather reanalysis and real-time weather forecasts (WRF and NCAR ensemble), airport station data, vegetation and electric grid characteristics and historical outage data to forecast the number and spatial distribution of outages in the power distribution grid located within dense vegetation. Recent OPM improvements consist of improved storm classification and addition of new predictive weather-related variables and are demonstrated using a leave-one-storm-out cross-validation based on 130 severe extratropical storms and two hurricanes (Sandy and Irene) in the Northeast US. We show that it is possible to predict the number of trouble spots causing outages in the electric grid with a median absolute percentage error as low as 27% for some storm types, and at most around 40%, in a scale that varies between four orders of magnitude, from few outages to tens of thousands. This outage information can be communicated to the electric utility to manage allocation of crews and equipment and minimize the recovery time for an upcoming storm hazard.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, northwest China

NASA Astrophysics Data System (ADS)

Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G.

2017-12-01

Snowmelt water is an important freshwater resource in the Altay Mountains in northwest China, and it is also crucial for local ecological system, economic and social sustainable development; however, warming climate and rapid spring snowmelt can cause floods that endanger both eco-environment and public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature-index model based on remote sensing coupled with high-resolution meteorological data obtained from NCEP reanalysis fields that were downscaled using Weather Research Forecasting model, then bias-corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of temperature-index model based on remote sensing were calibrated for spring 2014, and model performance was validated using MODIS snow cover and snow observations from spring 2012. The results show that the temperature-index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutchliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt runoff was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt runoff accounts for 72% of spring runoff and 21% of annual runoff. Snowmelt is the main source of runoff for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt runoff predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data is lacking.

Regional Eco-hydrologic Sensitivity to Projected Amazonian Land Use Scenarios

NASA Astrophysics Data System (ADS)

Knox, R. G.; Longo, M.; Zhang, K.; Levine, N. M.; Moorcroft, P. R.; Bras, R. L.

2011-12-01

Given business as usual land-use practices, it is estimated that by 2050 roughly half of the Amazon's pre-anthropogenic closed-canopy forest stands would remain. Of this, eight of the Amazon's twelve major hydrologic basins would lose more than half of their forest cover to deforestation. With the availability of these land-use projections, we may start to question the associated response of the region's hydrologic climate to significant land-cover change. Here the Ecosystem-Demography Model 2 (EDM2, a dynamic and spatially distributed terrestrial model of plant structure and composition, succession, disturbance and thermodynamic transfer) is coupled with the Brazilian Regional Atmospheric Model (BRAMS, a three-dimensional limited area model of the atmospheric fluid momentum equations and physics parameterizations for closing the system of equations at the lower boundary, convection, radiative transfer, microphysics, etc). This experiment conducts decadal simulations, framed with high-reliability lateral boundary conditions of reanalysis atmospheric data (ERA-40 interim) and variable impact of land-use scenarios (SimAmazonia). This is done by initializing the regional ecosystem structure with both aggressive and conservationist deforestation scenarios, and also by differentially allowing and not-allowing dynamic vegetation processes. While the lateral boundaries of the simulation will not reflect the future climate in the region, reanalysis data has provided improved realism as compared to results derived from GCM boundary data. Therefore, the ecosystem response (forest composition and structure) and the time-space patterns of hydrologic information (soil moisture, rainfall, evapotranspiration) are objectively compared in the context of a sensitivity experiment, as opposed to a forecast. The following questions are addressed. How do aggressive and conservative scenarios of Amazonian deforestation effect the regional patterning of hydrologic information in the Amazon and South American Convergence Zone, and does forest response in these regions influence that patterning of hydrologic information?

File Specification for the MERRA Aerosol Reanalysis (MERRAero): MODIS AOD Assimilation based on a MERRA Replay

NASA Technical Reports Server (NTRS)

Da Silva, A. M.; Randles, C. A.; Buchard, V.; Darmenov, A.; Colarco, P. R.; Govindaraju, R.

2015-01-01

This document describes the gridded output files produced by the Goddard Earth Observing System version 5 (GEOS-5) Goddard Aerosol Assimilation System (GAAS) from July 2002 through December 2014. The MERRA Aerosol Reanalysis (MERRAero) is produced with the hydrostatic version of the GEOS-5 Atmospheric Global Climate Model (AGCM). In addition to standard meteorological parameters (wind, temperature, moisture, surface pressure), this simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), ozone, carbon monoxide and carbon dioxide. This model simulation is driven by prescribed sea-surface temperature and sea-ice, daily volcanic and biomass burning emissions, as well as high-resolution inventories of anthropogenic emission sources. Meteorology is replayed from the MERRA Reanalysis.

A reanalysis dataset of the South China Sea.

PubMed

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992-2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability.

A reanalysis dataset of the South China Sea

PubMed Central

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

MERRA-2: File Specification

NASA Technical Reports Server (NTRS)

Bosilovich, M. G.; Lucchesi, R.; Suarez, M.

2015-01-01

The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is a NASA atmospheric reanalysis that begins in 1980. It replaces the original MERRA reanalysis (Rienecker et al., 2011) using an upgraded version of the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The file collections for MERRA-2 are described in detail in this document, including some important changes from those of the MERRA dataset (Lucchesi, 2012).

Spatiotemporal Variance of Global Horizontal Moisture Transport and the Influence of Strong ENSO Events Using ERA-Interim Reanalysis

NASA Astrophysics Data System (ADS)

Kutta, E. J.; Hubbart, J. A.; Svoma, B. M.; Eichler, T. P.; Lupo, A. R.

2016-12-01

El Nino-Southern Oscillation (ENSO) is well documented as a leading source of seasonal to inter-annual variations in global weather and climate. Strong ENSO events have been shown to alter the location and magnitude of Hadley and Walker circulations that maintain equilibrium at tropical latitudes and regulate moisture transport into mid-latitude storm tracks. Broad impacts associated with ENSO events include anomalous regional precipitation (ARP) and temperature patterns and subsequent impacts to socioeconomic and human health systems. Potential socioeconomic and human health impacts range from regional changes in water resources and agricultural productivity to local storm water management, particularly in rapidly urbanizing watersheds. Evidence is mounting to suggest that anthropogenic climate change will increase the frequency of heavy precipitation events, which compounds impacts of ARP patterns associated with strong El Nino events. Therefore, the need exists to identify common regional patterns of spatiotemporal variance of horizontal moisture flux (HMF) during months (Oct-Feb) associated with the peak intensity (Oceanic Nino Index [ONI]) of the three strongest El Nino (ONI > µ + 2σ) and La Nina (ONI < µ - σ) events occurring between January 1979 and June 2016. ERA-Interim reanalysis output on model levels was used to quantify spatial and temporal covariance of HMF at 6-hourly resolution before taking the density weighted vertical average. Long term means (LTM; 1979-2015) were quantified and the influence of strong ENSO events was assessed by quantifying deviations from the LTM for each respective covariance property during months associated with the selected ENSO events. Results reveal regions of statistically significant (CI = 0.05) differences from the LTM for the vertically integrated HMF and each covariance quantity. Broader implications of this work include potential for improved seasonal precipitation forecasts at regional scales and subsequent improvements to local water resource management. There is potential for future work objectively comparing these results with output from Earth System Models to improve representation of ENSO's influence on spatiotemporal variance of horizontal moisture transport.

Estimating trends in atmospheric water vapor and temperature time series over Germany

NASA Astrophysics Data System (ADS)

Alshawaf, Fadwa; Balidakis, Kyriakos; Dick, Galina; Heise, Stefan; Wickert, Jens

2017-08-01

Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between -1.5 and 2.3 mm decade-1 with standard deviations below 0.25 mm decade-1. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade-1 with standard errors below 0.03 mm decade-1. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius-Clapeyron equation.

Does using different modern climate datasets impact pollen-based paleoclimate reconstructions in North America during the past 2,000 years

NASA Astrophysics Data System (ADS)

Ladd, Matthew; Viau, Andre

2013-04-01

Paleoclimate reconstructions rely on the accuracy of modern climate datasets for calibration of fossil records under the assumption of climate normality through time, which means that the modern climate operates in a similar manner as over the past 2,000 years. In this study, we show how using different modern climate datasets have an impact on a pollen-based reconstruction of mean temperature of the warmest month (MTWA) during the past 2,000 years for North America. The modern climate datasets used to explore this research question include the: Whitmore et al., (2005) modern climate dataset; North American Regional Reanalysis (NARR); National Center For Environmental Prediction (NCEP); European Center for Medium Range Weather Forecasting (ECMWF) ERA-40 reanalysis; WorldClim, Global Historical Climate Network (GHCN) and New et al., which is derived from the CRU dataset. Results show that some caution is advised in using the reanalysis data on large-scale reconstructions. Station data appears to dampen out the variability of the reconstruction produced using station based datasets. The reanalysis or model-based datasets are not recommended for paleoclimate large-scale North American reconstructions as they appear to lack some of the dynamics observed in station datasets (CRU) which resulted in warm-biased reconstructions as compared to the station-based reconstructions. The Whitmore et al. (2005) modern climate dataset appears to be a compromise between CRU-based datasets and model-based datasets except for the ERA-40. In addition, an ultra-high resolution gridded climate dataset such as WorldClim may only be useful if the pollen calibration sites in North America have at least the same spatial precision. We reconstruct the MTWA to within +/-0.01°C by using an average of all curves derived from the different modern climate datasets, demonstrating the robustness of the procedure used. It may be that the use of an average of different modern datasets may reduce the impact of uncertainty of paleoclimate reconstructions, however, this is yet to be determined with certainty. Future evaluation using for example the newly developed Berkeley earth surface temperature datasets should be tested against the paleoclimate record.

High-spatial-resolution mapping of precipitable water vapour using SAR interferograms, GPS observations and ERA-Interim reanalysis

NASA Astrophysics Data System (ADS)

Tang, Wei; Liao, Mingsheng; Zhang, Lu; Li, Wei; Yu, Weimin

2016-09-01

A high spatial and temporal resolution of the precipitable water vapour (PWV) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally differenced maps of the spatial distribution of PWV by analysing the tropospheric delay "noise" in interferometric synthetic aperture radar (InSAR). Time series maps of differential PWV were obtained by processing a set of ENVISAT ASAR (Advanced Synthetic Aperture Radar) images covering the area of southern California, USA from 6 October 2007 to 29 November 2008. To get a more accurate PWV, the component of hydrostatic delay was calculated and subtracted by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapour. The InSAR-derived differential PWV maps were calibrated by means of the GPS PWV measurements over the study area. We validated our results against the measurements of PWV derived from the Medium Resolution Imaging Spectrometer (MERIS) which was located together with the ASAR sensor on board the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantage of the InSAR technique is that it provides water vapour distribution with a spatial resolution as fine as 20 m and an accuracy of ˜ 2 mm. Such high-spatial-resolution maps of PWV could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts. With the launch of Sentinel-1A and Sentinel-1B satellites, every few days (6 days) new SAR images can be acquired with a wide swath up to 250 km, enabling a unique operational service for InSAR-based water vapour maps with unprecedented spatial and temporal resolution.

Refinement of pollutant gas emissions in the state of Rio de Janeiro for applications in modeling air quality on a local scale

NASA Astrophysics Data System (ADS)

Domínguez Chovert, Angel; Félix Alonso, Marcelo; Frassoni, Ariane; José Ferreira, Valter; Eiras, Denis; Longo, Karla; Freitas, Saulo

2017-04-01

Numerical modeling is a fundamental tool for studying the earth system components along with weather and climate forecast. In fact, the development of on-line models allows to simulate conditions of the atmosphere, for example, to evaluate certain chemicals in weather events with the purpose of improving a region's quality of air. For this determined purpose, the on-line models employ information from a broad range of sources in order to generate its variables forecasts. But beyond vast information sources, for a region's quality of air study, the data concerning the amount and distribution of emissions of polluting gases must be representative, as well as, it's required complete georeferenced emissions for simulations made with high resolution. Consequently, the modifications made in this work to the PREP-CHEM-SRC (Preprocessor of trace gas and aerosol emission fields for regional and a global atmospheric chemistry models) tool are presented to meliorate the initialization files for BRAMS models, 5.2 version (Brazilian Developments on the Regional Atmospheric Modeling System) and WRF (Weather Research and Forecasting Model) with vehicle emissions in the state of Rio de Janeiro, Brazil. It was determined the annual vehicle emission, until the year 2030, of the nitrogen oxides species (NOx) and carbon monoxide (CO) for each city and using different scenarios. For Rio de Janeiro city, a process of distribution by emissions of the main pollutant gases was implemented. In total, five different types of routes were used and the emission percentage for each one was calculated using the most current traffic information in them. For to check the industrial contributions to the emissions were used the global datasets RETRO (REanalysis of TROpospheric chemical composition) and EDGAR-HTAP (Emission Database for Global Atmospheric Research). On the other hand, for the biogenic contributions was used information from the MEGAN model (Model of Emissions of Gases and Aerosols from Nature). For all the analyzed species it was possible to observe the strong influence of the vehicular activity on the emission distribution.

Humidity Bias and Effect on Simulated Aerosol Optical Properties during the Ganges Valley Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Yan; Cadeddu, M.; Kotamarthi, V. R.

2016-07-10

The radiosonde humidity profiles available during the Ganges Valley Experiment were compared to those simulated from the regional Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF -Chern) and the global reanalysis datasets. Large biases were revealed. On a monthly mean basis at Nainital, located in northern India, the WRFChern model simulates a large moist bias in the free troposphere (up to +20%) as well as a large dry bias in the boundary layer (up to -30%). While the overall pattern of the biases is similar, the magnitude of the biases varies from time to time andmore » from one location to another. At Thiruvananthapuram, the magnitude of the dry bias is smaller, and in contrast to Nainital, the higher-resolution regional WRF -Chern model generates larger moist biases in the upper troposphere than the global reanalysis data. Furthermore, the humidity biases in the upper troposphere, while significant, have little impact on the model estimation of column aerosol optical depth (AOD). The frequent occurrences of the dry boundary-layer bias simulated by the large-scale models tend to lead to the underestimation of AOD. It is thus important to quantify the humidity vertical profiles for aerosol simulations over South Asia.« less

The August 1975 Flood over Central China

NASA Astrophysics Data System (ADS)

Yang, Long; Smith, James; Liu, Maofeng; Baeck, MaryLynn

2016-04-01

The August 1975 flood in Central China was one of the most destructive floods in history, resulting in 26 000 fatalities, leaving about 10 million people with insufficient shelter, and producing long-lasting famine and disease. Extreme rainfall responsible for this flood event was associated with typhoon Nina during 5-7 August 1975. Despite the prominence of the August 1975 flood, analyses of the storms producing the flood and the resulting flood are sparse. Even fewer attempts were made from the perspective of numerical simulations. We examine details of extreme rainfall for the August 1975 flood based on downscaling simulations using the Weather Research and Forecasting (WRF) model driven by 20th Century Reanalysis fields. We further placed key hydrometeorological features for the flood event in a climatological context through the analyses of the 20th Century Reanalysis fields. Results indicate interrelated roles of multiple mesoscale ingredients for deep, moist convection in producing extreme rainfall for the August 1975 flood, superimposed over an anomalous synoptic environment. Attribution analyses on the source of water vapor for this flood event will be conducted based on a Lagrangian parcel tracking algorithm LAGRANTO. Analytical framework developed in this study aims to explore utilization of hydrometeorological approach in flood-control engineering designs by providing details on key elements of flood-producing storms.

Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51

NASA Astrophysics Data System (ADS)

Jöckel, Patrick; Tost, Holger; Pozzer, Andrea; Kunze, Markus; Kirner, Oliver; Brenninkmeijer, Carl A. M.; Brinkop, Sabine; Cai, Duy S.; Dyroff, Christoph; Eckstein, Johannes; Frank, Franziska; Garny, Hella; Gottschaldt, Klaus-Dirk; Graf, Phoebe; Grewe, Volker; Kerkweg, Astrid; Kern, Bastian; Matthes, Sigrun; Mertens, Mariano; Meul, Stefanie; Neumaier, Marco; Nützel, Matthias; Oberländer-Hayn, Sophie; Ruhnke, Roland; Runde, Theresa; Sander, Rolf; Scharffe, Dieter; Zahn, Andreas

2016-03-01

Three types of reference simulations, as recommended by the Chemistry-Climate Model Initiative (CCMI), have been performed with version 2.51 of the European Centre for Medium-Range Weather Forecasts - Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model: hindcast simulations (1950-2011), hindcast simulations with specified dynamics (1979-2013), i.e. nudged towards ERA-Interim reanalysis data, and combined hindcast and projection simulations (1950-2100). The manuscript summarizes the updates of the model system and details the different model set-ups used, including the on-line calculated diagnostics. Simulations have been performed with two different nudging set-ups, with and without interactive tropospheric aerosol, and with and without a coupled ocean model. Two different vertical resolutions have been applied. The on-line calculated sources and sinks of reactive species are quantified and a first evaluation of the simulation results from a global perspective is provided as a quality check of the data. The focus is on the intercomparison of the different model set-ups. The simulation data will become publicly available via CCMI and the Climate and Environmental Retrieval and Archive (CERA) database of the German Climate Computing Centre (DKRZ). This manuscript is intended to serve as an extensive reference for further analyses of the Earth System Chemistry integrated Modelling (ESCiMo) simulations.

An analysis of high-impact, low-predictive skill severe weather events in the northeast U.S

NASA Astrophysics Data System (ADS)

Vaughan, Matthew T.

An objective evaluation of Storm Prediction Center slight risk convective outlooks, as well as a method to identify high-impact severe weather events with poor-predictive skill are presented in this study. The objectives are to assess severe weather forecast skill over the northeast U.S. relative to the continental U.S., build a climatology of high-impact, low-predictive skill events between 1980--2013, and investigate the dynamic and thermodynamic differences between severe weather events with low-predictive skill and high-predictive skill over the northeast U.S. Severe storm reports of hail, wind, and tornadoes are used to calculate skill scores including probability of detection (POD), false alarm ratio (FAR) and threat scores (TS) for each convective outlook. Low predictive skill events are binned into low POD (type 1) and high FAR (type 2) categories to assess temporal variability of low-predictive skill events. Type 1 events were found to occur in every year of the dataset with an average of 6 events per year. Type 2 events occur less frequently and are more common in the earlier half of the study period. An event-centered composite analysis is performed on the low-predictive skill database using the National Centers for Environmental Prediction Climate Forecast System Reanalysis 0.5° gridded dataset to analyze the dynamic and thermodynamic conditions prior to high-impact severe weather events with varying predictive skill. Deep-layer vertical shear between 1000--500 hPa is found to be a significant discriminator in slight risk forecast skill where high-impact events with less than 31-kt shear have lower threat scores than high-impact events with higher shear values. Case study analysis of type 1 events suggests the environment over which severe weather occurs is characterized by high downdraft convective available potential energy, steep low-level lapse rates, and high lifting condensation level heights that contribute to an elevated risk of severe wind.

Generation of a Realistic Soil Moisture Initialization System and its Potential Impact on Short-to-Seasonal Forecasting of Near Surface Variables

NASA Astrophysics Data System (ADS)

Boisserie, M.; Cocke, S.; O'Brien, J. J.

2009-12-01

Although the amount of water contained in the soil seems insignificant when compared to the total amount of water on a global-scale, soil moisture is widely recognized as a crucial variable for climate studies. It plays a key role in regulating the interaction between the atmosphere and the land-surface by controlling the repartition between the surface latent and sensible heat fluxes. In addition, the persistence of soil moisture anomalies provides one of the most important components of memory for the climate system. Several studies have shown that, during the boreal summer in mid-latitudes, the soil moisture role in controlling the continental precipitation variability may be more important than that of the sea surface temperature (Koster et al. 2000, Hong and Kalnay 2000, Koster et al. 2000, Kumar and Hoerling 1995, Trenberth et al. 1998, Shukla 1998). Although all of the above studies have demonstrated the strong sensitivity of seasonal forecasts to the soil moisture initial conditions, they relied on extreme or idealized soil moisture levels. The question of whether realistic soil moisture initial conditions lead to improved seasonal predictions has not been adequately addressed. Progress in addressing this question has been hampered by the lack of long-term reliable observation-based global soil moisture data sets. Since precipitation strongly affects the soil moisture characteristics at the surface and in depth, an alternative to this issue is to assimilate precipitation. Because precipitation is a diagnostic variable, most of the current reanalyses do not directly assimilate it into their models (M. Bosilovitch, 2008). In this study, an effective technique that directly assimilates the precipitation is used. We examine two experiments. In the first experiment, the model is initialized by directly assimilating a global, 3-hourly, 1.0° precipitation dataset, provided by Sheffield et al. (2006), in a continuous assimilation period of a couple of months. For this, we use a technique named the Precipitation Assimilation Reanalysis (PAR) described in Nunes and Cocke (2004). This technique consists of modifying the vertical profile of humidity as a function of the observed and predicted model rain rates. In the second experiment, the model is initialized without precipitation assimilation. For each experiment, ten sets of seasonal forecasts of the coupled land-atmosphere Florida State University/Center for Ocean and Atmosphere Predictions Studies (FSU/COAPS) model were generated, starting from the boreal summer of each year between 1986 and 1995. For each forecast, ten ensembles are produced by starting the forecast from the 1st and the 15th of each month from April to August. The results of these experiments show, first, that the PAR technique greatly improves the temporal and spatial variability of out model soil moisture estimate. Second, using these realistic soil moisture initial conditions, we found a significant increase in the air temperature seasonal forecasting skills. However, not significant increase has been found in the precipitation seasonal forecasting skills. The results of this study are involved in the GLACE-2 international multi-model experiment.

Identifying Changes in the Probability of High Temperature, High Humidity Heat Wave Events

NASA Astrophysics Data System (ADS)

Ballard, T.; Diffenbaugh, N. S.

2016-12-01

Understanding how heat waves will respond to climate change is critical for adequate planning and adaptation. While temperature is the primary determinant of heat wave severity, humidity has been shown to play a key role in heat wave intensity with direct links to human health and safety. Here we investigate the individual contributions of temperature and specific humidity to extreme heat wave conditions in recent decades. Using global NCEP-DOE Reanalysis II daily data, we identify regional variability in the joint probability distribution of humidity and temperature. We also identify a statistically significant positive trend in humidity over the eastern U.S. during heat wave events, leading to an increased probability of high humidity, high temperature events. The extent to which we can expect this trend to continue under climate change is complicated due to variability between CMIP5 models, in particular among projections of humidity. However, our results support the notion that heat wave dynamics are characterized by more than high temperatures alone, and understanding and quantifying the various components of the heat wave system is crucial for forecasting future impacts.

Investigation of Kelvin wave periods during Hai-Tang typhoon using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Kishore, P.; Jayalakshmi, J.; Lin, Pay-Liam; Velicogna, Isabella; Sutterley, Tyler C.; Ciracì, Enrico; Mohajerani, Yara; Kumar, S. Balaji

2017-11-01

Equatorial Kelvin waves (KWs) are fundamental components of the tropical climate system. In this study, we investigate Kelvin waves (KWs) during the Hai-Tang typhoon of 2005 using Empirical Mode Decomposition (EMD) of regional precipitation, zonal and meridional winds. For the analysis, we use daily precipitation datasets from the Global Precipitation Climatology Project (GPCP) and wind datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-analysis (ERA-Interim). As an additional measurement, we use in-situ precipitation datasets from rain-gauges over the Taiwan region. The maximum accumulated precipitation was approximately 2400 mm during the period July 17-21, 2005 over the southwestern region of Taiwan. The spectral analysis using the wind speed at 950 hPa found in the 2nd, 3rd, and 4th intrinsic mode functions (IMFs) reveals prevailing Kelvin wave periods of ∼3 days, ∼4-6 days, and ∼6-10 days, respectively. From our analysis of precipitation datasets, we found the Kelvin waves oscillated with periods between ∼8 and 20 days.

The quasi 2 day wave response in TIME-GCM nudged with NOGAPS-ALPHA

NASA Astrophysics Data System (ADS)

Wang, Jack C.; Chang, Loren C.; Yue, Jia; Wang, Wenbin; Siskind, D. E.

2017-05-01

The quasi 2 day wave (QTDW) is a traveling planetary wave that can be enhanced rapidly to large amplitudes in the mesosphere and lower thermosphere (MLT) region during the northern winter postsolstice period. In this study, we present five case studies of QTDW events during January and February 2005, 2006 and 2008-2010 by using the Thermosphere-Ionosphere-Mesosphere Electrodynamics-General Circulation Model (TIME-GCM) nudged with the Navy Operational Global Atmospheric Prediction System-Advanced Level Physics High Altitude (NOGAPS-ALPHA) Weather Forecast Model. With NOGAPS-ALPHA introducing more realistic lower atmospheric forcing in TIME-GCM, the QTDW events have successfully been reproduced in the TIME-GCM. The nudged TIME-GCM simulations show good agreement in zonal mean state with the NOGAPS-ALPHA 6 h reanalysis data and the horizontal wind model below the mesopause; however, it has large discrepancies in the tropics above the mesopause. The zonal mean zonal wind in the mesosphere has sharp vertical gradients in the nudged TIME-GCM. The results suggest that the parameterized gravity wave forcing may need to be retuned in the assimilative TIME-GCM.

Hindcast of extreme sea states in North Atlantic extratropical storms

NASA Astrophysics Data System (ADS)

Ponce de León, Sonia; Guedes Soares, Carlos

2015-02-01

This study examines the variability of freak wave parameters around the eye of northern hemisphere extratropical cyclones. The data was obtained from a hindcast performed with the WAve Model (WAM) model forced by the wind fields of the Climate Forecast System Reanalysis (CFSR). The hindcast results were validated against the wave buoys and satellite altimetry data showing a good correlation. The variability of different wave parameters was assessed by applying the empirical orthogonal functions (EOF) technique on the hindcast data. From the EOF analysis, it can be concluded that the first empirical orthogonal function (V1) accounts for greater share of variability of significant wave height (Hs), peak period (Tp), directional spreading (SPR) and Benjamin-Feir index (BFI). The share of variance in V1 varies for cyclone and variable: for the 2nd storm and Hs V1 contains 96 % of variance while for the 3rd storm and BFI V1 accounts only for 26 % of variance. The spatial patterns of V1 show that the variables are distributed around the cyclones centres mainly in a lobular fashion.

Theoretical and software considerations for general dynamic analysis using multilevel substructured models

NASA Technical Reports Server (NTRS)

Schmidt, R. J.; Dodds, R. H., Jr.

1985-01-01

The dynamic analysis of complex structural systems using the finite element method and multilevel substructured models is presented. The fixed-interface method is selected for substructure reduction because of its efficiency, accuracy, and adaptability to restart and reanalysis. This method is extended to reduction of substructures which are themselves composed of reduced substructures. The implementation and performance of the method in a general purpose software system is emphasized. Solution algorithms consistent with the chosen data structures are presented. It is demonstrated that successful finite element software requires the use of software executives to supplement the algorithmic language. The complexity of the implementation of restart and reanalysis porcedures illustrates the need for executive systems to support the noncomputational aspects of the software. It is shown that significant computational efficiencies can be achieved through proper use of substructuring and reduction technbiques without sacrificing solution accuracy. The restart and reanalysis capabilities and the flexible procedures for multilevel substructured modeling gives economical yet accurate analyses of complex structural systems.

Implementing the national AIGA flash flood warning system in France

NASA Astrophysics Data System (ADS)

Organde, Didier; Javelle, Pierre; Demargne, Julie; Arnaud, Patrick; Caseri, Angelica; Fine, Jean-Alain; de Saint Aubin, Céline

2015-04-01

The French national hydro-meteorological and flood forecasting centre (SCHAPI) aims to implement a national flash flood warning system to improve flood alerts for small-to-medium (up to 1000 km2) ungauged basins. This system is based on the AIGA method, co-developed by IRSTEA these last 10 years. The method, initially set up for the Mediterranean area, is based on a simple event-based hourly hydrologic distributed model run every 15 minutes (Javelle et al. 2014). The hydrologic model ingests operational radar-gauge rainfall grids from Météo-France at a 1-km² resolution to produce discharges for successive outlets along the river network. Discharges are then compared to regionalized flood quantiles of given return periods and warnings (expressed as the range of the return period estimated in real-time) are provided on a river network map. The main interest of the method is to provide forecasters and emergency services with a synthetic view in real time of the ongoing flood situation, information that is especially critical in ungauged flood prone areas. In its enhanced national version, the hourly event-based distributed model is coupled to a continuous daily rainfall-runoff model which provides baseflow and a soil moisture index (for each 1-km² pixel) at the beginning of the hourly simulation. The rainfall-runoff models were calibrated on a selection of 700 French hydrometric stations with Météo-France radar-gauge reanalysis dataset for the 2002-2006 period. To estimate model parameters for ungauged basins, the 2 hydrologic models were regionalised by testing both regressions (using different catchment attributes, such as catchment area, soil type, and climate characteristic) and spatial proximity techniques (transposing parameters from neighbouring donor catchments), as well as different homogeneous hydrological areas. The most valuable regionalisation method was determined for each model through jack-knife cross-validation. The system performance was then evaluated with contingency criteria (e.g., Critical Success Index, Probability Of Detection, Success Ratio) using operational rainfall radar-gauge products from Météo-France for the 2009-2012 period. The regionalised parameters of the distributed model were finally adjusted for each homogeneous hydrological area to optimize the Heidke skill score (HSS) calculated with three levels of warnings (2-, 10- and 50-year flood quantiles). This work is currently being implemented by the SCHAPI to set up an automated national flash flood warning system by 2016. Planned improvements include developing a unique continuous model to be run at a sub-hourly timestep, discharge assimilation, as well as integrating precipitation forecasts while accounting for the main sources of forecast uncertainty. Javelle, P., Demargne, J., Defrance, D., and Arnaud, P. 2014. Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, DOI: 10.1080/02626667.2014.923970

Monitoring the Global Soil Moisture Climatology Using GLDAS/LIS

NASA Astrophysics Data System (ADS)

Meng, J.; Mitchell, K.; Wei, H.; Gottschalck, J.

2006-05-01

Soil moisture plays a crucial role in the terrestrial water cycle through governing the process of partitioning precipitation among infiltration, runoff and evaporation. Accurate assessment of soil moisture and other land states, namely, soil temperature, snowpack, and vegetation, is critical in numerical environmental prediction systems because of their regulation of surface water and energy fluxes between the surface and atmosphere over a variety of spatial and temporal scales. The Global Land Data Assimilation System (GLDAS) is developed, jointly by NASA Goddard Space Flight Center (GSFC) and NOAA National Centers for Environmental Prediction (NCEP), to perform high-quality global land surface simulation using state-of-art land surface models and further minimizing the errors of simulation by constraining the models with observation- based precipitation, and satellite land data assimilation techniques. The GLDAS-based Land Information System (LIS) infrastructure has been installed on the NCEP supercomputer that serves the operational weather and climate prediction systems. In this experiment, the Noah land surface model is offline executed within the GLDAS/LIS infrastructure, driven by the NCEP Global Reanalysis-2 (GR2) and the CPC Merged Analysis of Precipitation (CMAP). We use the same Noah code that is coupled to the operational NCEP Global Forecast System (GFS) for weather prediction and test bed versions of the NCEP Climate Forecast System (CFS) for seasonal prediction. For assessment, it is crucial that this uncoupled GLDAS/Noah uses exactly the same Noah code (and soil and vegetation parameters therein), and executes with the same horizontal grid, landmask, terrain field, soil and vegetation types, seasonal cycle of green vegetation fraction and surface albedo as in the coupled GFS/Noah and CFS/Noah. This execution is for the 25-year period of 1980-2005, starting with a pre-execution 10-year spin-up. This 25-year GLDAS/Noah global land climatology will be used for both climate variability assessment and as a source of land initial conditions for ensemble CFS/Noah seasonal hindcast experiments. Finally, this GLDAS/Noah climatology will serve as the foundation for a global drought/flood monitoring system that includes near realtime daily updates of the global land states.

The high-resolution regional reanalysis COSMO-REA6

NASA Astrophysics Data System (ADS)

Ohlwein, C.

2016-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers the past 20 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

A high-resolution regional reanalysis for Europe

NASA Astrophysics Data System (ADS)

Ohlwein, C.

2015-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers the past 20 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

The Use of Red Green Blue Air Mass Imagery to Investigate the Role of Stratospheric Air in a Non-convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Jedlovec, G. J.; Molthan, A. L.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

The Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary; Elmer, Nicholas

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Folmer, M. J.; Jedlovec, G. J.

2014-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), 32-km North American Regional Reanalysis (NARR) interpolated to a 12-km grid, and 13-km Rapid Refresh analyses.

Impact of the Assimilation of Hyperspectral Infrared Retrieved Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Jedlovec, G. J.

2014-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis, and Rapid Refresh analyses.

A Study on the Potential Applications of Satellite Data in Air Quality Monitoring and Forecasting

NASA Technical Reports Server (NTRS)

Li, Can; Hsu, N. Christina; Tsay, Si-Chee

2011-01-01

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be necessa1Y before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.

The NASA Reanalysis Ensemble Service - Advanced Capabilities for Integrated Reanalysis Access and Intercomparison

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.

2017-12-01

NASA's efforts to advance climate analytics-as-a-service are making new capabilities available to the research community: (1) A full-featured Reanalysis Ensemble Service (RES) comprising monthly means data from multiple reanalysis data sets, accessible through an enhanced set of extraction, analytic, arithmetic, and intercomparison operations. The operations are made accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib; (2) A cloud-based, high-performance Virtual Real-Time Analytics Testbed supporting a select set of climate variables. This near real-time capability enables advanced technologies like Spark and Hadoop-based MapReduce analytics over native NetCDF files; and (3) A WPS-compliant Web service interface to our climate data analytics service that will enable greater interoperability with next-generation systems such as ESGF. The Reanalysis Ensemble Service includes the following: - New API that supports full temporal, spatial, and grid-based resolution services with sample queries - A Docker-ready RES application to deploy across platforms - Extended capabilities that enable single- and multiple reanalysis area average, vertical average, re-gridding, standard deviation, and ensemble averages - Convenient, one-stop shopping for commonly used data products from multiple reanalyses including basic sub-setting and arithmetic operations (e.g., avg, sum, max, min, var, count, anomaly) - Full support for the MERRA-2 reanalysis dataset in addition to, ECMWF ERA-Interim, NCEP CFSR, JMA JRA-55 and NOAA/ESRL 20CR… - A Jupyter notebook-based distribution mechanism designed for client use cases that combines CDSlib documentation with interactive scenarios and personalized project management - Supporting analytic services for NASA GMAO Forward Processing datasets - Basic uncertainty quantification services that combine heterogeneous ensemble products with comparative observational products (e.g., reanalysis, observational, visualization) - The ability to compute and visualize multiple reanalysis for ease of inter-comparisons - Automated tools to retrieve and prepare data collections for analytic processing

Reforecasting the 1972-73 ENSO Event and the Monsoon Drought Over India

NASA Astrophysics Data System (ADS)

Shukla, J.; Huang, B.; Shin, C. S.

2016-12-01

This paper presents the results of reforcasting the 1972-73 ENSO event and the Indian summer monsoon drought using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), initialized with the Eu­ropean Centre for Medium-Range Weather Forecasts (ECMWF) global ocean reanalysis version 4, and observation-based land and atmosphere reanalyses. The results of this paper demonstrate that if the modern day climate models were available during the 1970's, even with the limited observations at that time, it should have been possible to predict the 1972-73 ENSO event and the associated monsoon drought. These results further suggest the necessity of continuing to develop realistic models of the climate system for accurate and reliable seasonal predictions. This paper also presents a comparison of the 1972-73 El Niño reforecast with the 1997-98 case. As the strongest event during 1958-78, the 1972-73 El Niño is distinguished from the 1997-98 one by its early termination. Initialized in the spring season, the forecast system predicted the onset and development of both events reasonably well, although the reforecasts underestimate the ENSO peaking magnitudes. On the other hand, the reforecasts initialized in spring and fall of 1972 persistently predicted lingering wind and SST anomalies in the eastern equatorial Pacific during the spring of 1973. Initialized in fall of 1997, the reforecast also grossly overestimates the peaking westerly wind and warm SST anomalies in the 1997-98 El Niño.In 1972-73, both the Eastern Pacific SST anomalies (for example Nino 3 Index) and the summer monsoon drought over India and the adjoining areas were predicted remarkably well. In contrast, the Eastern Pacific SST anomalies for the 1997-98 event were predicted well, but the normal summer monsoon rainfall over India of 1997 was not predicted by the model. This case study of the 1972-73 event is part of a larger, comprehensive reforecast project undertaken by one of the coauthors (Bohua Huang, see the paper by Huang et al. Reforecasting the ENSO Events in the Past Fifty-Seven Years (1958-2014) in another AGU session) in which seasonal hindcasts are being carried out for each of the 57 years (1958-2014) using CFSv2.

Report of the 4th World Climate Research Programme International Conference on Reanalyses

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Rixen, Michel; van Oevelen, Peter; Asrar, Ghassem; Compo, Gilbert; Onogi, Kazutoshi; Simmons, Adrian; Trenberth, Kevin; Behringer, Dave; Bhuiyan, Tanvir Hossain;

GOW2.0: A global wave hindcast of high resolution

NASA Astrophysics Data System (ADS)

Menendez, Melisa; Perez, Jorge; Losada, Inigo

2016-04-01

The information provided by reconstructions of historical wind generated waves is of paramount importance for a variety of coastal and offshore purposes (e.g. risk assessment, design of costal structures and coastal management). Here, a new global wave hindcast (GOW2.0) is presented. This hindcast is an update of GOW1.0 (Reguero et al. 2012) motivated by the emergence of new settings and atmospheric information from reanalysis during recent years. GOW2.0 is based on version 4.18 of WaveWatch III numerical model (Tolman, 2014). Main features of the model set-up are the analysis and selection of recent source terms concerning wave generation and dissipation (Ardhuin et al. 2010, Zieger et al., 2015) and the implementation of obstruction grids to improve the modeling of wave shadowing effects in line with the approach described in Chawla and Tolman (2007). This has been complemented by a multigrid system and the use of the hourly wind and ice coverage from the Climate Forecast System Reanalysis, CFSR (30km spatial resolution approximately). The multigrid scheme consists of a series of "two-way" nested domains covering the whole ocean basins at a 0.5° spatial resolution and continental shelfs worldwide at a 0.25° spatial resolution. In addition, a technique to reconstruct wave 3D spectra for any grid-point is implemented from spectral partitioning information. A validation analysis of GOW2.0 outcomes has been undertaken considering wave spectral information from surface buoy stations and multi-mission satellite data for a spatial validation. GOW2.0 shows a substantial improvement over its predecessor for all the analyzed variables. In summary, GOW2.0 reconstructs historical wave spectral data and climate information from 1979 to present at hourly resolution providing higher spatial resolution over regions where local generated wind seas, bimodal-spectral behaviour and relevant swell transformations across the continental shelf are important. Ardhuin F, Rogers E, Babanin AV, et al (2010). Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation. J Phys Oceanogr. 2010;40(9):1917-1941. doi:10.1175/2010JPO4324.1. Chawla A, Tolman HL. Obstruction grids for spectral wave models. Ocean Model. 2008;22(1-2):12-25. doi:10.1016/j.ocemod.2008.01.003. Reguero BG, Menendez M, Mendez FJ, Minguez R, Losada IJ (2012). A Global Ocean Wave (GOW) calibrated reanalysis from 1948 onwards. Coastal Engineering, 65, 38-55. Tolman HL (2014). User manual and system documentation of WAVEWATCH III version 4.18. NOAA / NWS / NCEP / MMAB Tech Note. Zieger S, Babanin AV, Rogers WE, Young IR (2015). Observation-based source terms in the third-generation wave model WAVEWATCH. Ocean Modelling, 96, 2-25.

The impact of large-scale circulation patterns on summer crop yields in IP

NASA Astrophysics Data System (ADS)

Capa Morocho, Mirian; Rodríguez Fonseca, Belén; Ruiz Ramos, Margarita

2014-05-01

Large-scale circulations patterns (ENSO, NAO) have been shown to have a significant impact on seasonal weather, and therefore on crop yield over many parts of the world(Garnett and Khandekar, 1992; Aasa et al., 2004; Rozas and Garcia-Gonzalez, 2012). In this study, we analyze the influence of large-scale circulation patterns and regional climate on the principal components of maize yield variability in Iberian Peninsula (IP) using reanalysis datasets. Additionally, we investigate the modulation of these relationships by multidecadal patterns. This study is performed analyzing long time series of maize yield, only climate dependent, computed with the crop model CERES-maize (Jones and Kiniry, 1986) included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5). To simulate yields, reanalysis daily data of radiation, maximum and minimum temperature and precipitation were used. The reanalysis climate data were obtained from National Center for Environmental Prediction (20th Century and NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) data server (ERA 40 and ERA Interim). Simulations were run at five locations: Lugo (northwestern), Lerida (NE), Madrid (central), Albacete (southeastern) and Córdoba (S IP) (Gabaldón et al., 2013). From these time series standardized anomalies were calculated. Afterwards, time series were time filtered to focus on the interannual-to-multiannual variability, splitting up in two components: low frequency (LF) and high frequency (HF) time scales. The principal components of HF yield anomalies in IP were compared with a set of documented patterns. These relationships were compared with multidecadal patterns, as Atlanctic Multidecadal Oscillations (AMO) and Interdecadal Pacific Oscillations (IPO). The results of this study have important implications in crop forecasting. In this way, it may have a positive impact on both public (agricultural planning) and private (decision support to farmers, insurance companies) sectors, to take advantage of favorable conditions or reduce the effect of adverse conditions. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Aasa, A., Jaagus, J., Ahas, R. and Sepp, M. 2004. The influence of atmospheric circulation on plant phenological phases in central and eastern Europe. International Journal of Climatology 24, 1551-1564. Gabaldón, C. et al. 2013. Evaluation of local strategies to climate change of maize crop in Andalusia for the first half of 21st century. European Geosciences Union - General Assembly2013 Vol. 15 (Vienna - Austria, 2013). Garnett, E. R. and Khandekar, M. L. 1992. The impact of large-scale atmospheric circulations and anomalies on Indian monsoon droughts and floods and on world grain yields-a statistical analysis. Agricultural and Forest Meteorology 61, 113-128. Jones, C. and Kiniry, J. 1986. CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, 194. Rozas, V. and Garcia-Gonzalez, I. 2012. Non-stationary influence of El Nino-Southern Oscillation and winter temperature on oak latewood growth in NW Iberian Peninsula. Int J Biometeorol 56, 787-800.

Use of the NASA GEOS-5 SEAC4RS Meteorological and Aerosol Reanalysis for assessing simulated aerosol optical properties as a function of smoke age

NASA Astrophysics Data System (ADS)

Randles, C. A.; da Silva, A. M., Jr.; Colarco, P. R.; Darmenov, A.; Buchard, V.; Govindaraju, R.; Chen, G.; Hair, J. W.; Russell, P. B.; Shinozuka, Y.; Wagner, N.; Lack, D.

2014-12-01

The NASA Goddard Earth Observing System version 5 (GEOS-5) Earth system model, which includes an online aerosol module, provided chemical and weather forecasts during the SEAC4RS field campaign. For post-mission analysis, we have produced a high resolution (25 km) meteorological and aerosol reanalysis for the entire campaign period. In addition to the full meteorological observing system used for routine NWP, we assimilate 550 nm aerosol optical depth (AOD) derived from MODIS (both Aqua and Terra satellites), ground-based AERONET sun photometers, and the MISR instrument (over bright surfaces only). Daily biomass burning emissions of CO, CO2, SO2, and aerosols are derived from MODIS fire radiative power retrievals. We have also introduced novel smoke "age" tracers, which provide, for a given time, a snapshot histogram of the age of simulated smoke aerosol. Because GEOS-5 assimilates remotely sensed AOD data, it generally reproduces observed (column) AOD compared to, for example, the airborne 4-STAR instrument. Constraining AOD, however, does not imply a good representation of either the vertical profile or the aerosol microphysical properties (e.g., composition, absorption). We do find a reasonable vertical structure for aerosols is attained in the model, provided actual smoke injection heights are not much above the planetary boundary layer, as verified with observations from DIAL/HRSL aboard the DC8. The translation of the simulated aerosol microphysical properties to total column AOD, needed in the aerosol assimilation step, is based on prescribed mass extinction efficiencies that depend on wavelength, composition, and relative humidity. Here we also evaluate the performance of the simulated aerosol speciation by examining in situ retrievals of aerosol absorption/single scattering albedo and scattering growth factor (f(RH)) from the LARGE and AOP suite of instruments. Putting these comparisons in the context of smoke age as diagnosed by the model helps us to revise assumed aerosol optical properties for an improved representation of aerosol radiative forcing.

Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

NASA Astrophysics Data System (ADS)

Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

2015-04-01

Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations from a variety of CMIP5 model ensembles. Here, we present results for the UK 2013/14 winter floods as proof of concept and we show validation and testing results that demonstrate the robustness of our method. We also revisit the record temperatures over Europe in 2014 and present a detailed analysis of this attribution exercise as it is one of the events to demonstrate that we can make a sensible statement of how the odds for such a year to occur have changed while it still unfolds.

MiKlip-PRODEF: Probabilistic Decadal Forecast for Central and Western Europe

NASA Astrophysics Data System (ADS)

Reyers, Mark; Haas, Rabea; Ludwig, Patrick; Pinto, Joaquim

2013-04-01

The demand for skilful climate predictions on time-scales of several years to decades has increased in recent years, in particular for economic, societal and political terms. Within the BMBF MiKlip consortium, a decadal prediction system on the global to local scale is currently being developed. The subproject PRODEF is part of the MiKlip-Module C, which aims at the regionalisation of decadal predictability for Central and Western Europe. In PRODEF, a combined statistical-dynamical downscaling (SDD) and a probabilistic forecast tool are developed and applied to the new Earth system model of the Max-Planck Institute Hamburg (MPI-ESM), which is part of the CMIP5 experiment. Focus is given on the decadal predictability of windstorms, related wind gusts as well as wind energy potentials. SDD combines the benefits of both high resolution dynamical downscaling and purely statistical downscaling of GCM output. Hence, the SDD approach is used to obtain a very large ensemble of highly resolved decadal forecasts. With respect to the focal points of PRODEF, a clustering of temporal evolving atmospheric fields, a circulation weather type (CWT) analysis, and a storm damage indices analysis is applied to the full ensemble of the decadal hindcast experiments of the MPI-ESM in its lower resolution (MPI-ESM-LR). The ensemble consists of up to ten realisations per yearly initialised decadal hindcast experiments for the period 1960-2010 (altogether 287 realisations). Representatives of CWTs / clusters and single storm episodes are dynamical downscaled with the regional climate model COSMO-CLM with a horizontal resolution of 0.22°. For each model grid point, the distributions of the local climate parameters (e.g. surface wind gusts) are determined for different periods (e.g. each decades) by recombining dynamical downscaled episodes weighted with the respective weather type frequencies. The applicability of the SDD approach is illustrated with examples of decadal forecasts of the MPI-ESM. We are able to perform a bias correction of the frequencies of large scale weather types and to quantify the uncertainties of decadal predictability on large and local scale arising from different initial conditions. Further, probability density functions of local parameters like e.g. wind gusts for different periods and decades derived from the SDD approach is compared to observations and reanalysis data. Skill scores are used to quantify the decadal predictability for different leading time periods and to analyse whether the SDD approach shows systematic errors for some regions.

Analysis and Hindcast Experiments of the 2009 Sudden Stratospheric Warming in WACCMX+DART

NASA Astrophysics Data System (ADS)

Pedatella, N. M.; Liu, H.-L.; Marsh, D. R.; Raeder, K.; Anderson, J. L.; Chau, J. L.; Goncharenko, L. P.; Siddiqui, T. A.

2018-04-01

The ability to perform data assimilation in the Whole Atmosphere Community Climate Model eXtended version (WACCMX) is implemented using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. Results are presented demonstrating that WACCMX+DART analysis fields reproduce the middle and upper atmosphere variability during the 2009 major sudden stratospheric warming (SSW) event. Compared to specified dynamics WACCMX, which constrains the meteorology by nudging toward an external reanalysis, the large-scale dynamical variability of the stratosphere, mesosphere, and lower thermosphere is improved in WACCMX+DART. This leads to WACCMX+DART better representing the downward transport of chemical species from the mesosphere into the stratosphere following the SSW. WACCMX+DART also reproduces most aspects of the observed variability in ionosphere total electron content and equatorial vertical plasma drift during the SSW. Hindcast experiments initialized on 5, 10, 15, 20, and 25 January are used to assess the middle and upper atmosphere predictability in WACCMX+DART. A SSW, along with the associated middle and upper atmosphere variability, is initially predicted in the hindcast initialized on 15 January, which is ˜10 days prior to the warming. However, it is not until the hindcast initialized on 20 January that a major SSW is forecast to occur. The hindcast experiments reveal that dominant features of the total electron content can be forecasted ˜10-20 days in advance. This demonstrates that whole atmosphere models that properly account for variability in lower atmosphere forcing can potentially extend the ionosphere-thermosphere forecast range.

High resolution modelling of wind fields for optimization of empirical storm flood predictions

NASA Astrophysics Data System (ADS)

Brecht, B.; Frank, H.

2014-05-01

High resolution wind fields are necessary to predict the occurrence of storm flood events and their magnitude. Deutscher Wetterdienst (DWD) created a catalogue of detailed wind fields of 39 historical storms at the German North Sea coast from the years 1962 to 2011. The catalogue is used by the Niedersächsisches Landesamt für Wasser-, Küsten- und Naturschutz (NLWKN) coastal research center to improve their flood alert service. The computation of wind fields and other meteorological parameters is based on the model chain of the DWD going from the global model GME via the limited-area model COSMO with 7 km mesh size down to a COSMO model with 2.2 km. To obtain an improved analysis COSMO runs are nudged against observations for the historical storms. The global model GME is initialised from the ERA reanalysis data of the European Centre for Medium-Range Weather Forecasts (ECMWF). As expected, we got better congruency with observations of the model for the nudging runs than the normal forecast runs for most storms. We also found during the verification process that different land use data sets could influence the results considerably.

Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine

NASA Astrophysics Data System (ADS)

Franch, B.; Vermote, E.; Roger, J. C.; Skakun, S.; Becker-Reshef, I.; Justice, C. O.

2017-12-01

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season and the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data. These methods were applied to MODIS CMG data in Ukraine, the US and China with errors around 10%. However, the NDVI is saturated for yield values higher than 4 MT/ha. As a consequence, the model had to be re-calibrated in each country and the validation of the national yields showed low correlation coefficients. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national yield of winter wheat in the United States and Ukraine from 2001 to 2016.

Synoptic Storms in the North Atlantic in the Atmospheric Reanalysis and Scatterometer-Based Wind Products

NASA Astrophysics Data System (ADS)

Dukhovskoy, D. S.; Bourassa, M. A.

2016-12-01

The study compares and analyses the characteristics of synoptic storms in the Subpolar North Atlantic over the time period from 2000 through 2009 derived from reanalysis data sets and scatterometer-based gridded wind products. The analysis is performed for ocean 10-m winds derived from the following wind data sets: NCEP/DOE AMIP-II reanalysis (NCEPR2), NCAR/CFSR, Arctic System Reanalysis (ASR) version 1, Cross-Calibrated Multi-Platform (CCMP) wind product versions 1.1 and recently released version 2.0 prepared by the Remote Sensing Systems, and QuikSCAT. A cyclone tracking algorithm employed in this study for storm identification is based on average vorticity fields derived from the wind data. The study discusses storm characteristics such as storm counts, trajectories, intensity, integrated kinetic energy, spatial scale. Interannal variability of these characteristics in the data sets is compared. The analyses demonstrates general agreement among the wind data products on the characteristics of the storms, their spatial distribution and trajectories. On average, the NCEPR2 storms are more energetic mostly due to large spatial scales and stronger winds. There is noticeable interannual variability in the storm characteristics, yet no obvious trend in storms is observed in the data sets.

Assessment of Bias in the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Reanalysis Radar-Only Estimate

NASA Astrophysics Data System (ADS)

Nelson, B. R.; Prat, O. P.; Stevens, S. E.; Seo, D. J.; Zhang, J.; Howard, K.

2014-12-01

The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is nearly completed for the period covering from 2001 to 2012. Reanalysis data are available at 1-km and 5-minute resolution. An important step in generating the best possible precipitation data is to assess the bias in the radar-only product. In this work, we use data from a combination of rain gauge networks to assess the bias in the NMQ reanalysis. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network Daily (GHCN-D) are combined for use in the assessment. These rain gauge networks vary in spatial density and temporal resolution. The challenge hence is to optimally utilize them to assess the bias at the finest resolution possible. For initial assessment, we propose to subset the CONUS data in climatologically representative domains, and perform bias assessment using information in the Q2 dataset on precipitation type and phase.

Extending Climate Analytics-As to the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; McInerney, M.; Nadeau, D.; Li, J.; Strong, S.; Thompson, J. H.

2015-12-01

We are building three extensions to prior-funded work on climate analytics-as-a-service that will benefit the Earth System Grid Federation (ESGF) as it addresses the Big Data challenges of future climate research: (1) We are creating a cloud-based, high-performance Virtual Real-Time Analytics Testbed supporting a select set of climate variables from six major reanalysis data sets. This near real-time capability will enable advanced technologies like the Cloudera Impala-based Structured Query Language (SQL) query capabilities and Hadoop-based MapReduce analytics over native NetCDF files while providing a platform for community experimentation with emerging analytic technologies. (2) We are building a full-featured Reanalysis Ensemble Service comprising monthly means data from six reanalysis data sets. The service will provide a basic set of commonly used operations over the reanalysis collections. The operations will be made accessible through NASA's climate data analytics Web services and our client-side Climate Data Services (CDS) API. (3) We are establishing an Open Geospatial Consortium (OGC) WPS-compliant Web service interface to our climate data analytics service that will enable greater interoperability with next-generation ESGF capabilities. The CDS API will be extended to accommodate the new WPS Web service endpoints as well as ESGF's Web service endpoints. These activities address some of the most important technical challenges for server-side analytics and support the research community's requirements for improved interoperability and improved access to reanalysis data.

Large Scale Skill in Regional Climate Modeling and the Lateral Boundary Condition Scheme

NASA Astrophysics Data System (ADS)

Veljović, K.; Rajković, B.; Mesinger, F.

2009-04-01

Several points are made concerning the somewhat controversial issue of regional climate modeling: should a regional climate model (RCM) be expected to maintain the large scale skill of the driver global model that is supplying its lateral boundary condition (LBC)? Given that this is normally desired, is it able to do so without help via the fairly popular large scale nudging? Specifically, without such nudging, will the RCM kinetic energy necessarily decrease with time compared to that of the driver model or analysis data as suggested by a study using the Regional Atmospheric Modeling System (RAMS)? Finally, can the lateral boundary condition scheme make a difference: is the almost universally used but somewhat costly relaxation scheme necessary for a desirable RCM performance? Experiments are made to explore these questions running the Eta model in two versions differing in the lateral boundary scheme used. One of these schemes is the traditional relaxation scheme, and the other the Eta model scheme in which information is used at the outermost boundary only, and not all variables are prescribed at the outflow boundary. Forecast lateral boundary conditions are used, and results are verified against the analyses. Thus, skill of the two RCM forecasts can be and is compared not only against each other but also against that of the driver global forecast. A novel verification method is used in the manner of customary precipitation verification in that forecast spatial wind speed distribution is verified against analyses by calculating bias adjusted equitable threat scores and bias scores for wind speeds greater than chosen wind speed thresholds. In this way, focusing on a high wind speed value in the upper troposphere, verification of large scale features we suggest can be done in a manner that may be more physically meaningful than verifications via spectral decomposition that are a standard RCM verification method. The results we have at this point are somewhat limited in view of the integrations having being done only for 10-day forecasts. Even so, one should note that they are among very few done using forecast as opposed to reanalysis or analysis global driving data. Our results suggest that (1) running the Eta as an RCM no significant loss of large-scale kinetic energy with time seems to be taking place; (2) no disadvantage from using the Eta LBC scheme compared to the relaxation scheme is seen, while enjoying the advantage of the scheme being significantly less demanding than the relaxation given that it needs driver model fields at the outermost domain boundary only; and (3) the Eta RCM skill in forecasting large scales, with no large scale nudging, seems to be just about the same as that of the driver model, or, in the terminology of Castro et al., the Eta RCM does not lose "value of the large scale" which exists in the larger global analyses used for the initial condition and for verification.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Final Technical Report for Collaborative Research: Developing and Implementing Ocean-Atmosphere Reanalyses for Climate Applications (OARCA)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Compo, Gilbert P

As an important step toward a coupled data assimilation system for generating reanalysis fields needed to assess climate model projections, the Ocean Atmosphere Coupled Reanalysis for Climate Applications (OARCA) project assesses and improves the longest reanalyses currently available of the atmosphere and ocean: the 20th Century Reanalysis Project (20CR) and the Simple Ocean Data Assimilation with sparse observational input (SODAsi) system, respectively. In this project, we make off-line but coordinated improvements in the 20CR and SODAsi datasets, with improvements in one feeding into improvements of the other through an iterative generation of new versions. These datasets now span from themore » 19th to 21st centuries. We then study the extreme weather and variability from days to decades of the resulting datasets. A total of 24 publications have been produced in this project.« less

An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences

NASA Astrophysics Data System (ADS)

Lynch, Peng; Reid, Jeffrey S.; Westphal, Douglas L.; Zhang, Jianglong; Hogan, Timothy F.; Hyer, Edward J.; Curtis, Cynthia A.; Hegg, Dean A.; Shi, Yingxi; Campbell, James R.; Rubin, Juli I.; Sessions, Walter R.; Turk, F. Joseph; Walker, Annette L.

2016-04-01

While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003-2013 global 1 × 1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.

Development studies towards an 11-year global gridded aerosol optical thickness reanalysis for climate and applied applications

NASA Astrophysics Data System (ADS)

Lynch, P.; Reid, J. S.; Westphal, D. L.; Zhang, J.; Hogan, T. F.; Hyer, E. J.; Curtis, C. A.; Hegg, D. A.; Shi, Y.; Campbell, J. R.; Rubin, J. I.; Sessions, W. R.; Turk, F. J.; Walker, A. L.

2015-12-01

While standalone satellite and model aerosol products see wide utilization, there is a significant need in numerous climate and applied applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003-2013 global 1° × 1° and 6 hourly modal aerosol optical thickness (AOT) reanalysis product. This dataset can be applied to basic and applied earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine and coarse mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite retrieved precipitation, rather than the model field. The final reanalyzed fine and coarse mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine and coarse mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.

Comparison of MERRA-2 and ECCO-v4 ocean surface heat fluxes: Consequences of different forcing feedbacks on ocean circulation and implications for climate data assimilation.

NASA Astrophysics Data System (ADS)

Strobach, E.; Molod, A.; Menemenlis, D.; Forget, G.; Hill, C. N.; Campin, J. M.; Heimbach, P.

2017-12-01

Forcing ocean models with reanalysis data is a common practice in ocean modeling. As part of this practice, prescribed atmospheric state variables and interactive ocean SST are used to calculate fluxes between the ocean and the atmosphere. When forcing an ocean model with reanalysis fields, errors in the reanalysis data, errors in the ocean model and errors in the forcing formulation will generate a different solution compared to other ocean reanalysis solutions (which also have their own errors). As a first step towards a consistent coupled ocean-atmosphere reanalysis, we compare surface heat fluxes from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to heat fluxes from a state-of-the-art oceanic reanalysis, the Estimating the Circulation and Climate of the Ocean Version 4, Release 2 (ECCO-v4). Then, we investigate the errors associated with the MITgcm ocean model in its ECCO-v4 ocean reanalysis configuration (1992-2011) when it is forced with MERRA-2 atmospheric reanalysis fields instead of with the ECCO-v4 adjoint optimized ERA-interim state variables. This is done by forcing ECCO-v4 ocean with and without feedbacks from MERRA-2 related to turbulent fluxes of heat and moisture and the outgoing long wave radiation. In addition, we introduce an intermediate forcing method that includes only the feedback from the interactive outgoing long wave radiation. The resulting ocean circulation is compared with ECCO-v4 reanalysis and in-situ observations. We show that, without feedbacks, imbalances in the energy and the hydrological cycles of MERRA-2 (which are directly related to the fact it was created without interactive ocean) result in considerable SST drifts and a large reduction in sea level. The bulk formulae and interactive outgoing long wave radiation, although providing air-sea feedbacks and reducing model-data misfit, strongly relax the ocean to observed SST and may result in unwanted features such as large change in the water budget. These features have implications in on desired forcing recipe to be used. The results strongly and unambiguously argue for next generation data assimilation climate studies to involve fully coupled systems.

El Niño suppresses Antarctic warming

NASA Astrophysics Data System (ADS)

Bertler, Nancy A. N.; Barrett, Peter J.; Mayewski, Paul A.; Fogt, Ryan L.; Kreutz, Karl J.; Shulmeister, James

2004-08-01

Here we present new isotope records derived from snow samples from the McMurdo Dry Valleys, Antarctica and re-analysis data of the European Centre for Medium-Range Weather Forecasts (ERA-40) to explain the connection between the warming of the Pacific sector of the Southern Ocean [Jacka and Budd, 1998; Jacobs et al., 2002] and the current cooling of the terrestrial Ross Sea region [Doran et al., 2002a]. Our analysis confirms previous findings that the warming is linked to the El Niño Southern Oscillation (ENSO) [Kwok and Comiso, 2002a, 2002b; Carleton, 2003; Ribera and Mann, 2003; Turner, 2004], and provides new evidence that the terrestrial cooling is caused by a simultaneous ENSO driven change in atmospheric circulation, sourced in the Amundsen Sea and West Antarctica.

Satellite-Enhanced Dynamical Downscaling of Extreme Events

NASA Astrophysics Data System (ADS)

Nunes, A.

2015-12-01

Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical downscaling of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical downscaling of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to downscale extreme weather occurrences. Scale-selective bias correction is a method employed in the downscaling, similar to the spectral nudging technique, in which the downscaled solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the downscaling of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.

Recent Precipitation Trends Over the Southern Ocean in Relation to Oceanic Freshening Near Antarctica

NASA Astrophysics Data System (ADS)

Cullather, R. I.; Jacobs, S. S.; Giulivi, C. F.; Leonard, K. C.; Stammerjohn, S. E.

2008-12-01

Quantitative assessments of large-scale precipitation over the world's oceanic regions are problematic, particularly for significant regions of the data-sparse Southern Hemisphere. Available data sets are based on the assimilation of land-based measurements, satellite radiance values, numerical weather forecast models, or some combination of the three. In this study we examine several products that cover most or all of the satellite era 1979-2007 over the Southern Ocean and surrounding mid-latitudes to 45°S. These include CMAP, the NCEP Reanalysis II, ERA-40, GPCP version 2, and the Japanese Re-analysis. Averaged fields from these data show large discrepancies in the mean spatial depiction and the annual cycle. Comparisons with unique in situ snowfall measurements and satellite-derived accumulation on sea ice are presented. The available record of oceanographic measurements in the Ross Sea indicates that salinity below 200 m in the Ross Sea has decreased by 0.03 per decade since 1958, with the highest (lowest) values in 1967 (2000). The fields examined here suggest that precipitation is likely not directly influencing the oceanic freshening observed in the Ross Sea, or in other coastal seas adjacent to Antarctica. The salinity anomaly is consistent with increasing attrition of continental ice, but places a heavy demand on the melt rate. Potential contributions to oceanic freshening from changes in sea ice extent, transport, and thickness are discussed.

Evaluation of a High-Resolution Regional Reanalysis for Europe

NASA Astrophysics Data System (ADS)

Ohlwein, C.; Wahl, S.; Keller, J. D.; Bollmeyer, C.

2014-12-01

Reanalyses gain more and more importance as a source of meteorological information for many purposes and applications. Several global reanalyses projects (e.g., ERA, MERRA, CSFR, JMA9) produce and verify these data sets to provide time series as long as possible combined with a high data quality. Due to a spatial resolution down to 50-70km and 3-hourly temporal output, they are not suitable for small scale problems (e.g., regional climate assessment, meso-scale NWP verification, input for subsequent models such as river runoff simulations). The implementation of regional reanalyses based on a limited area model along with a data assimilation scheme is able to generate reanalysis data sets with high spatio-temporal resolution. Within the Hans-Ertel-Centre for Weather Research (HErZ), the climate monitoring branch concentrates efforts on the assessment and analysis of regional climate in Germany and Europe. In joint cooperation with DWD (German Meteorological Service), a high-resolution reanalysis system based on the COSMO model has been developed. The regional reanalysis for Europe matches the domain of the CORDEX EURO-11 specifications, albeit at a higher spatial resolution, i.e., 0.055° (6km) instead of 0.11° (12km) and comprises the assimilation of observational data using the existing nudging scheme of COSMO complemented by a special soil moisture analysis with boundary conditions provided by ERA-Interim data. The reanalysis data set covers 6 years (2007-2012) and is currently extended to 16 years. Extensive evaluation of the reanalysis is performed using independent observations with special emphasis on precipitation and high-impact weather situations indicating a better representation of small scale variability. Further, the evaluation shows an added value of the regional reanalysis with respect to the forcing ERA Interim reanalysis and compared to a pure high-resolution dynamical downscaling approach without data assimilation.

Web-based Reanalysis Intercomparison Tools (WRIT): Comparing Reanalyses and Observational data.

NASA Astrophysics Data System (ADS)

Compo, G. P.; Smith, C. A.; Hooper, D. K.

2014-12-01

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions, with metadata, such as variable name and units, that also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, re-grid if needed, and convert units. Comparing reanalyses with observational datasets is difficult for similar reasons. Even if a dataset can be read via Open-source Project for a Network Data Access Protocol (OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. To overcome some of the obstacles in reanalysis intercomparison, our group at the Cooperative Institute for Research in the Environmental Sciences (CIRES) at the University of Colorado and affiliated colleagues at National Oceanic and Atmospheric Administration's (NOAA's) Earth System Research Laboratory Physical Sciences Division (ESRL/PSD) have created a set of Web-based Reanalysis Intercomparison Tools (WRIT) at http://www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis and observational datasets, and to test hypotheses. Currently, there are tools to plot monthly mean maps and vertical cross-sections, timeseries, and trajectories for standard pressure level and surface variables. Users can refine dates, statistics, and plotting options. Reanalysis datasets currently available include the NCEP/NCAR R1, NCEP/DOE R2, MERRA, ERA-Interim, NCEP CFSR and the 20CR. Observational datasets include those containing precipitation (e.g. GPCP), temperature (e.g. GHCNCAMS), winds (e.g. WASWinds), precipitable water (e.g. NASA NVAP), SLP (HadSLP2), and SST (NOAA ERSST). WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

NASA Astrophysics Data System (ADS)

Rieckh, Therese; Anthes, Richard; Randel, William; Ho, Shu-Peng; Foelsche, Ulrich

2018-05-01

While water vapor is the most important tropospheric greenhouse gas, it is also highly variable in both space and time, and water vapor concentrations range over 3 orders of magnitude in the troposphere. These properties challenge all observing systems to accurately measure and resolve the vertical structure and variability of tropospheric humidity. In this study we characterize the humidity measurements of various observing techniques, including four separate Global Positioning System (GPS) radio occultation (RO) humidity retrievals (University Corporation for Atmospheric Research (UCAR) direct, UCAR one-dimensional variational retrieval (1D-Var), Wegener Center for Climate and Global Change (WEGC) 1D-Var, Jet Propulsion Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and NCEP Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we analyzed time-height cross sections over four radiosonde stations in the tropical and subtropical western Pacific for the year 2007. We found that the accuracy of RO humidity is comparable to or better than both radiosonde and AIRS humidity over 800 to 400 hPa, as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000-400 hPa layer, and differences are most pronounced above 600 hPa.

NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2008-01-01

This talk will review the status and progress of the NASA/Global Modeling and Assimilation Office (GMAO) atmospheric global reanalysis project called the Modern Era Retrospective-Analysis for Research and Applications (MERRA). An overview of NASA's emerging capabilities for assimilating a variety of other Earth Science observations of the land, ocean, and atmospheric constituents will also be presented. MERRA supports NASA Earth science by synthesizing the current suite of research satellite observations in a climate data context (covering the period 1979-present), and by providing the science and applications communities with of a broad range of weather and climate data with an emphasis on improved estimates of the hydrological cycle. MERRA is based on a major new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5), that includes the Earth System Modeling Framework (ESMF)-based GEOS-5 atmospheric general circulation model and the new NOAA National Centers for Environmental Prediction (NCEP) unified grid-point statistical interpolation (GST) analysis scheme developed as a collaborative effort between NCEP and the GMAO. In addition to MERRA, the GMAO is developing new capabilities in aerosol and constituent assimilation, ocean, ocean biology, and land surface assimilation. This includes the development of an assimilation capability for tropospheric air quality monitoring and prediction, the development of a carbon-cycle modeling and assimilation system, and an ocean data assimilation system for use in coupled short-term climate forecasting.

The NASA Modern Era Reanalysis for Research and Applications, Version-2 (MERRA-2)

NASA Astrophysics Data System (ADS)

Gelaro, R.; McCarty, W.; Molod, A.; Suarez, M.; Takacs, L.; Todling, R.

2014-12-01

The NASA Modern Era Reanalysis for Research Applications Version-2 (MERRA-2) is a reanalysis for the satellite era using an updated version of the Goddard Earth Observing System Data Assimilation System Version-5 (GEOS-5) produced by the Global Modeling and Assimilation Office (GMAO). MERRA-2 will assimilate meteorological and aerosol observations not available to MERRA and includes improvements to the GEOS-5 model and analysis scheme so as to provide an ongoing climate analysis beyond MERRA's terminus. MERRA-2 will also serve as a development milestone for a future GMAO coupled Earth system analysis. Production of MERRA-2 began in June 2014 in four processing streams, with convergence to a single near-real time climate analysis expected by early 2015. This talk provides an overview of the MERRA-2 system developments and key science results. For example, compared with MERRA, MERRA-2 exhibits a well-balanced relationship between global precipitation and evaporation, with significantly reduced sensitivity to changes in the global observing system through time. Other notable improvements include reduced biases in the tropical middle- and upper-tropospheric wind and near-surface temperature over continents.

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

Relating isotopic composition of precipitation to atmospheric patterns and local moisture recycling

NASA Astrophysics Data System (ADS)

Logan, K. E.; Brunsell, N. A.; Nippert, J. B.

2016-12-01

Local land management practices such as irrigation significantly alter surface evapotranspiration (ET), regional boundary layer development, and potentially modify precipitation likelihood and amount. How strong this local forcing is in comparison to synoptic-scale dynamics, and how much ET is recycled locally as precipitation are areas of great uncertainty and are especially important when trying to forecast the impact of local land management strategies on drought mitigation. Stable isotope analysis has long been a useful tool for tracing movement throughout the water cycle. In this study, reanalysis data and stable isotope samples of precipitation events are used to estimate the contribution of local moisture recycling to precipitation at the Konza Prairie LTER - located in the Great Plains, downwind of intensive agricultural areas. From 2001 to 2014 samples of all precipitation events over 5mm were collected and 18O and D isotopes measured. Comparison of observed precipitation totals and MERRA and ERA-interim reanalysis totals is used to diagnose periods of strong local moisture contribution (especially from irrigation) to precipitation. Large discrepancies in precipitation between observation and reanalysis, particularly MERRA, tend to follow dry periods during the growing season, presumably because while ERA-Interim adjusts soil moisture using observed surface temperature and humidity, MERRA includes no such local soil moisture adjustment and therefore lacks potential precipitation feedbacks induced by irrigation. The δ18O and δD signature of local irrigation recycling is evaluated using these incongruous observations. Self-organizing maps (SOM) are then used to identify a comprehensive range of synoptic conditions that result in precipitation at Konza LTER. Comparison of isotopic signature and SOM classification of rainfall events allows for identification of the primary moisture source and estimation of the contribution of locally recycled moisture. The climatology of precipitation source and changes in the influence of local moisture over the course of 14 years of observation are explored.

Interdecadal changes in the Asian winter monsoon variability and its relationship with ENSO and AO

NASA Astrophysics Data System (ADS)

Yun, Kyung-Sook; Seo, Ye-Won; Ha, Kyung-Ja; Lee, June-Yi; Kajikawa, Yoshiyuki

2014-08-01

Interdecadal changes in the Asian winter monsoon (AWM) variability are investigated using three surface air temperature datasets for the 55-year period of 1958-2012 from (1) the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 1 (NCEP), (2) combined datasets from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-yr reanalysis and interim data (ERA), and (3) Japanese 55-year reanalysis (JRA). Particular attention has been paid to the first four empirical orthogonal function (EOF) modes of the AWM temperature variability that together account for 64% of the total variance and have been previously identified as predictable modes. The four modes are characterized as follows: the first mode by a southern warming over the Indo-western Pacific Ocean associated with a gradually increasing basin-wide warming trend; the second mode by northern warming with the interdecadal change after the late 1980s; the third and fourth modes by north-south triple pattern, which reveal a phase shift after the late 1970s. The three reanalyses agree well with each other when producing the first three modes, but show large discrepancy in capturing both spatial and temporal characteristics of the fourth mode. It is therefore considered that the first three leading modes are more reliable than the rest higher modes. Considerable interdecadal changes are found mainly in the first two modes. While the first mode shows gradually decreasing variance, the second mode exhibits larger interannual variance during the recent decade. In addition, after the late 1970s, the first mode has a weakening relationship with the El Niño-Southern Oscillation (ENSO) whereas the second mode has strengthening association with the Artic Oscillation (AO). This indicates an increasing role of AO but decreasing role of ENSO on the AWM variability. A better understanding of the interdecadal change in the dominant modes would contribute toward advancing in seasonal prediction and the predictability of the AWM variability.

Optimizing Placement of Weather Stations: Exploring Objective Functions of Meaningful Combinations of Multiple Weather Variables

NASA Astrophysics Data System (ADS)

Snyder, A.; Dietterich, T.; Selker, J. S.

2017-12-01

Many regions of the world lack ground-based weather data due to inadequate or unreliable weather station networks. For example, most countries in Sub-Saharan Africa have unreliable, sparse networks of weather stations. The absence of these data can have consequences on weather forecasting, prediction of severe weather events, agricultural planning, and climate change monitoring. The Trans-African Hydro-Meteorological Observatory (TAHMO.org) project seeks to address these problems by deploying and operating a large network of weather stations throughout Sub-Saharan Africa. To design the TAHMO network, we must determine where to place weather stations within each country. We should consider how we can create accurate spatio-temporal maps of weather data and how to balance the desired accuracy of each weather variable of interest (precipitation, temperature, relative humidity, etc.). We can express this problem as a joint optimization of multiple weather variables, given a fixed number of weather stations. We use reanalysis data as the best representation of the "true" weather patterns that occur in the region of interest. For each possible combination of sites, we interpolate the reanalysis data between selected locations and calculate the mean average error between the reanalysis ("true") data and the interpolated data. In order to formulate our multi-variate optimization problem, we explore different methods of weighting each weather variable in our objective function. These methods include systematic variation of weights to determine which weather variables have the strongest influence on the network design, as well as combinations targeted for specific purposes. For example, we can use computed evapotranspiration as a metric that combines many weather variables in a way that is meaningful for agricultural and hydrological applications. We compare the errors of the weather station networks produced by each optimization problem formulation. We also compare these errors to those of manually designed weather station networks in West Africa, planned by the respective host-country's meteorological agency.

Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation

NASA Astrophysics Data System (ADS)

Kerry, Colette; Powell, Brian; Roughan, Moninya; Oke, Peter

2016-10-01

As with other Western Boundary Currents globally, the East Australian Current (EAC) is highly variable making it a challenge to model and predict. For the EAC region, we combine a high-resolution state-of-the-art numerical ocean model with a variety of traditional and newly available observations using an advanced variational data assimilation scheme. The numerical model is configured using the Regional Ocean Modelling System (ROMS 3.4) and takes boundary forcing from the BlueLink ReANalysis (BRAN3). For the data assimilation, we use an Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) scheme, which uses the model dynamics to perturb the initial conditions, atmospheric forcing, and boundary conditions, such that the modelled ocean state better fits and is in balance with the observations. This paper describes the data assimilative model configuration that achieves a significant reduction of the difference between the modelled solution and the observations to give a dynamically consistent "best estimate" of the ocean state over a 2-year period. The reanalysis is shown to represent both assimilated and non-assimilated observations well. It achieves mean spatially averaged root mean squared (rms) residuals with the observations of 7.6 cm for sea surface height (SSH) and 0.4 °C for sea surface temperature (SST) over the assimilation period. The time-mean rms residual for subsurface temperature measured by Argo floats is a maximum of 0.9 °C between water depths of 100 and 300 m and smaller throughout the rest of the water column. Velocities at several offshore and continental shelf moorings are well represented in the reanalysis with complex correlations between 0.8 and 1 for all observations in the upper 500 m. Surface radial velocities from a high-frequency radar array are assimilated and the reanalysis provides surface velocity estimates with complex correlations with observed velocities of 0.8-1 across the radar footprint. A comparison with independent (non-assimilated) shipboard conductivity temperature depth (CTD) cast observations shows a marked improvement in the representation of the subsurface ocean in the reanalysis, with the rms residual in potential density reduced to about half of the residual with the free-running model in the upper eddy-influenced part of the water column. This shows that information is successfully propagated from observed variables to unobserved regions as the assimilation system uses the model dynamics to adjust the model state estimate. This is the first study to generate a reanalysis of the region at such a high resolution, making use of an unprecedented observational data set and using an assimilation method that uses the time-evolving model physics to adjust the model in a dynamically consistent way. As such, the reanalysis potentially represents a marked improvement in our ability to capture important circulation dynamics in the EAC. The reanalysis is being used to study EAC dynamics, observation impact in state-estimation, and as forcing for a variety of downscaling studies.

10 CFR 50.46 - Acceptance criteria for emergency core cooling systems for light-water nuclear power reactors.

Code of Federal Regulations, 2010 CFR

2010-01-01

... schedule for providing a reanalysis or taking other action as may be needed to show compliance with § 50.46... schedule for providing a reanalysis or taking other action as may be needed to show compliance with § 50.46... generated from the chemical reaction of the cladding with water or steam shall not exceed 0.01 times the...

Drivers and seasonal predictability of extreme wind speeds in the ECMWF System 4 and a statistical model

NASA Astrophysics Data System (ADS)

Walz, M. A.; Donat, M.; Leckebusch, G. C.

2017-12-01

As extreme wind speeds are responsible for large socio-economic losses in Europe, a skillful prediction would be of great benefit for disaster prevention as well as for the actuarial community. Here we evaluate patterns of large-scale atmospheric variability and the seasonal predictability of extreme wind speeds (e.g. >95th percentile) in the European domain in the dynamical seasonal forecast system ECMWF System 4, and compare to the predictability based on a statistical prediction model. The dominant patterns of atmospheric variability show distinct differences between reanalysis and ECMWF System 4, with most patterns in System 4 extended downstream in comparison to ERA-Interim. The dissimilar manifestations of the patterns within the two models lead to substantially different drivers associated with the occurrence of extreme winds in the respective model. While the ECMWF System 4 is shown to provide some predictive power over Scandinavia and the eastern Atlantic, only very few grid cells in the European domain have significant correlations for extreme wind speeds in System 4 compared to ERA-Interim. In contrast, a statistical model predicts extreme wind speeds during boreal winter in better agreement with the observations. Our results suggest that System 4 does not seem to capture the potential predictability of extreme winds that exists in the real world, and therefore fails to provide reliable seasonal predictions for lead months 2-4. This is likely related to the unrealistic representation of large-scale patterns of atmospheric variability. Hence our study points to potential improvements of dynamical prediction skill by improving the simulation of large-scale atmospheric dynamics.

WRF-based fire risk modelling and evaluation for years 2010 and 2012 in Poland

NASA Astrophysics Data System (ADS)

Stec, Magdalena; Szymanowski, Mariusz; Kryza, Maciej

2016-04-01

Wildfires are one of the main ecosystems' disturbances for forested, seminatural and agricultural areas. They generate significant economic loss, especially in forest management and agriculture. Forest fire risk modeling is therefore essential e.g. for forestry administration. In August 2015 a new method of forest fire risk forecasting entered into force in Poland. The method allows to predict a fire risk level in a 4-degree scale (0 - no risk, 3 - highest risk) and consists of a set of linearized regression equations. Meteorological information is used as predictors in regression equations, with air temperature, relative humidity, average wind speed, cloudiness and rainfall. The equations include also pine litter humidity as a measure of potential fuel characteristics. All these parameters are measured routinely in Poland at 42 basic and 94 auxiliary sites. The fire risk level is estimated for a current (basing on morning measurements) or next day (basing on midday measurements). Entire country is divided into 42 prognostic zones, and fire risk level for each zone is taken from the closest measuring site. The first goal of this work is to assess if the measurements needed for fire risk forecasting may be replaced by the data from mesoscale meteorological model. Additionally, the use of a meteorological model would allow to take into account much more realistic spatial differentiation of weather elements determining the fire risk level instead of discrete point-made measurements. Meteorological data have been calculated using the Weather Research and Forecasting model (WRF). For the purpose of this study the WRF model is run in the reanalysis mode allowing to estimate all required meteorological data in a 5-kilometers grid. The only parameter that cannot be directly calculated using WRF is the litter humidity, which has been estimated using empirical formula developed by Sakowska (2007). The experiments are carried out for two selected years: 2010 and 2012. The year 2010 was characterized by the smallest number of wildfires and burnt area whereas 2012 - by the biggest number of fires and the largest area of conflagration. The data about time, localization, scale and causes of individual wildfire occurrence in given years are taken from the National Forest Fire Information System (KSIPL), administered by Forest Fire Protection Department of Polish Forest Research Institute. The database is a part of European Forest Fire Information System (EFFIS). Basing on this data and on the WRF-based fire risk modelling we intend to achieve the second goal of the study, which is the evaluation of the forecasted fire risk with an occurrence of wildfires. Special attention is paid here to the number, time and the spatial distribution of wildfires occurred in cases of low-level predicted fire risk. Results obtained reveals the effectiveness of the new forecasting method. The outcome of our investigation allows to draw a conclusion that some adjustments are possible to improve the efficiency on the fire-risk estimation method.

Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

Projecting Future Changes in Extreme Weather During the North American Monsoon in the Southwest with High Resolution, Convective-Permitting Regional Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Chang, H. I.; Castro, C. L.; Luong, T. M.; Lahmers, T.; Jares, M.; Carrillo, C. M.

2014-12-01

Most severe weather during the North American monsoon in the Southwest U.S. occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. Our objective is to project how monsoon severe weather is changing due to anthropogenic global warming. We first consider a dynamically downscaled reanalysis (35 km grid spacing), generated with the Weather Research and Forecasting (WRF) model during the period 1948-2010. Individual severe weather events, identified by favorable thermodynamic conditions of instability and precipitable water, are then simulated for short-term, numerical weather prediction-type simulations of 24h at a convective-permitting scale (2 km grid spacing). Changes in the character of severe weather events within this period likely reflect long-term climate change driven by anthropogenic forcing. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future and if these changes correspond with what is already occurring per the downscaled renalaysis and available observational data. The CMIP5 models we are downscaling (HadGEM and MPI-ECHAM6) will be included as part of North American CORDEX. The regional model experimental design for severe weather event projection reasonably accounts for the known operational forecast prerequisites for severe monsoon weather. The convective-permitting simulations show that monsoon convection appears to be reasonably well captured with the use of the dynamically downscaled reanalysis, in comparison to Stage IV precipitation data. The regional model tends to initiate convection too early, though correctly simulates the diurnal maximum in convection in the afternoon and subsequent westward propagation of thunderstorms. Projected changes in extreme event precipitation will be described in relation to the long-term changes in thermodynamic and dynamic forcing mechanisms for severe weather. Results from this project will be used for climate change impacts assessment for U.S. military installations in the region.

The Black Sea Monitoring and Forecasting Center (BS-MFC) in the framework of the Copernicus Marine Service

NASA Astrophysics Data System (ADS)

Palazov, Atanas; Coppini, Giovanni; Ciliberti, Stefania Angela; Gregoire, Marilaure; Staneva, Joanna; Peneva, Elisaveta; Özsoy, Emin; Vandenbulcke, Luc; Storto, Andrea; Lemieux-Dudon, Benedicte; Lovato, Tomas; Masina, Simona; Pinardi, Nadia; Palermo, Francesco; Creti, Sergio; Macchia, Francesca; Lecci, Rita; Behrens, Arno; Marinova, Veselka; Slabakova, Violeta

2017-04-01

The BS-MFC entered the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/) in October 2016, providing regular and systematic information about the ocean state in the Black Sea in operational mode. An expert team constitutes the BS-MFC Consortium: the Institute of Oceanology, Bulgarian Academy of Sciences (IO-BAS, Bulgaria) coordinates the service and the management in collaboration with Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC, Italy), Helmholtz-Zentrum Geesthacht - Institute of Coastal Research (HZG, Germany), the University of Liege (ULG, Belgium), the Sofia University "St. Kliment Ohridski (USOF, Bulgaria) and the Eurasia Earth Sciences Institute - Istanbul Technical University (ITU, Turkey). The system provides a complete data catalogue for the Black Sea ocean variables such as temperature, salinity, sea level, currents, biogeochemistry and waves through a technologically advanced and resilient service, which is fully interconnected with the other Centers in the Copernicus network. The high level BS-MFC architecture is based on 3 Production Units, for Physics, Biogeochemistry and Waves products respectively, a Dissemination/Archiving Unit for the delivery of the products and their archiving/accessibility, a Local Service Desk connected to the CMEMS Service Desk devoted to support all the operational activities, and backup units for all the main service components. Products consist of analysis/hindcast, 10-days forecast and reanalysis, describing the physical (currents, temperature, salinity, sea level, mixed layer depth and bottom temperature), the biogeochemical state and waves. To implement and improve the service, the BS-MFC has detailed an evolution plan, actually under implementation, devoted to establish, assess and improve the systems and their operational functionalities, providing some improvements from the scientific point of view concerning the modeling components (e.g., the fully aligned Physics, Biogeochemistry and Waves systems, the open boundary conditions at the Bosporus, the state-of-the-art core models and increased spatial resolution among the major actions) and high quality products, towards an optimal interface between the Mediterranean and the Black Seas. The contribution will present the main operational and research & development activities at the basis of the systems, given an overview on the future plans for improving the service for the delivery of new products.

What the diurnal cycle of precipitation tells us about land-atmosphere coupling strength

NASA Astrophysics Data System (ADS)

Ferguson, Craig; Song, Hyojong; Roundy, Joshua

2015-04-01

The key attributes of a coupled forecast model are the coupling strengths between the land-atmosphere and ocean-atmosphere schemes. If a model cannot skillfully capture the diurnal cycle of clouds and precipitation, then it likely cannot be expected to yield accurate long-term climate projections. The seasonal drought forecast skill shortfalls of the U.S. NCEP Coupled Forecast System Version 2 (CFSv2) have been directly linked to its unrealistically strong land-atmosphere coupling strength. Most models can be similarly categorized, which is to say, sensitivity to the land physics (i.e., soil moisture constraints on evapotranspiration) is too strong. In nature, the land signal: noise ratio appears to be at a much lower value. Diagnosing land-atmosphere coupling strength requires at a minimum: surface soil moisture state, surface turbulent heat fluxes, and atmospheric moisture and instability. Full-on diagnosis would entail hacking into the code and inserting a number of tracers. This study addresses the question: What if, given the soil wetness anomaly, model biases in coupling sign and/or strength could be diagnosed from phase shifts in the diurnal precipitation frequency cycle? We use 34-years of output from the North American Regional Reanalysis (NARR) and North American Land Data Assimilation System Phase 2 (NLDAS-2) to investigate the variation in diurnal precipitation frequency cycle between so-called "wet-advantage" and "dry-advantage" coupling regimes over the U.S. southern Great Plains. Wet-advantage occurs when the atmospheric state is closer to the wet adiabatic rate and convection is triggered by a strong increase in the moist static energy from the surface. In contrast, dry-advantage occurs when the atmosphere is drier and the temperature profile is close to the dry adiabatic lapse rate, which favors convection over areas of large boundary layer growth due to high sensible heat fluxes at the surface. We find that there is a significant difference in the phase of the diurnal precipitation frequency between coupling regimes. Specifically, maximum frequency occurs at 1600 LT and 0500 LT for wet- and dry-advantage samples, respectively. For each of these contrasting regimes, we investigate the relative extent to which diurnal phasing may be attributed to local land -- PBL processes versus influences of the Great Plains low-level jet and large-scale atmospheric circulation.

Understanding the role of moisture transport on the dry bias in indian monsoon simulations by CFSv2

NASA Astrophysics Data System (ADS)

Sahana, A. S.; Pathak, Amey; Roxy, M. K.; Ghosh, Subimal

2018-02-01

We analyse the bias present in the Indian Summer Monsoon Rainfall (ISMR), as simulated by Climate Forecast System Model 2 (CFSv2), the operational model used for monsoon forecasts in India. In the simulations, the precipitation intensity is redistributed within the ITCZ band with southward shifts of precipitation maxima. We observe weakening of maximum intensity of precipitation over the region between 20°N and 14°N. In the simulations by CFSv2, there exists two rain bands: the northern one located slightly southward compared to reanalysis dataset and the southern one over the equator with intensified precipitation. This results in dry bias over land and wet bias over the ocean. We use a Dynamic Recycling Model, based on Lagrangian approach, to investigate the role of various moisture sources in generating these biases. We find that, the dry bias during June exists due to the delayed monsoon onset and reduced moisture flow from the Arabian Sea. As the monsoon progresses, deficiency in the simulated contributions from South Indian Ocean becomes the key source of bias. The reduced supply of moisture from oceanic sources is primarily attributed to the weaker northward transport of moisture flux from the Southern Ocean, associated with a weaker southward energy flux. Inefficiency of the model in simulating the heating in Tibetan plateau during the pre-monsoon period leads to this reduced cross equatorial energy flow. We also find that, towards the end of monsoon season, moisture contributions from land sources namely, Ganga Basin and North-Eastern forests become significant and underestimations of the same in the simulations by CFSv2 result into biases over Central and Eastern India.

Enhancing access and usage of earth observations to support environmental decision making in Eastern and Southern Africa

NASA Astrophysics Data System (ADS)

Shukla, S.; Husak, G. J.; Macharia, D.; Peterson, P.; Landsfeld, M. F.; Funk, C.; Flores, A.

2017-12-01

Remote sensing, reanalysis and model based earth observations (EOs) are crucial for environmental decision making, particularly in a region like Eastern and Southern Africa, where ground-based observations are sparse. NASA and the Famine Early Warning System Network (FEWS NET) provide several EOs relevant for monitoring, providing early warning of agroclimatic conditions. Nonetheless, real-time application of those EOs for decision making in the region is still limited. This presentation reports on an ongoing SERVIR-supported Applied Science Team (AST) project that aims to fill that gap by working in close collaboration with Regional Centre for Mapping of Resources for Development (RCMRD), the NASA SERVIR regional hub. The three main avenues being taken to enhance access and usage of EOs in the region are: (1) Transition and implementation of web-based tools to RCMRD to allow easy processing and visualization of EOs (2) Capacity building of personnel from regional and national agroclimate service agencies in using EOs, through training using targeted case studies, and (3) Development of new datasets to meet the specific needs of RCMRD and regional stakeholders. The presentation will report on the initial success, lessons learned, and feedback thus far in this project regarding the implementation of web-based tool and capacity building efforts. It will also briefly describe three new datasets, currently in development, to improve agroclimate monitoring in the region, which are: (1) Satellite infrared and stations based temperature maximum dataset (CHIRTS) (2) NASA's GEOS5 and NCEP's CFSv2 based seasonal scale reference evapotranspiration forecasts and (3) NCEP's GEFS based medium range weather forecasts which are bias-corrected to USGS and UCSB's rainfall monitoring dataset (CHIRPS).

Global trends in significant wave height and marine wind speed from the ERA-20CM

NASA Astrophysics Data System (ADS)

Aarnes, Ole Johan; Breivik, Øyvind

2016-04-01

The ERA-20CM is one of the latest additions to the ERA-series produced at the European Center for Medium-Range Weather Forecasts (ECMWF). This 10 member ensemble is generated with a version of the Integrated Forecast System (IFS), a coupled atmosphere-wave model. The model integration is run as a AMIP (Atmospheric Model Intercomparison Project) constrained by CMIP5 recommended radiative forcing and different realizations of sea-surface temperature (SST) and sea-ice cover (SIC) prescribed by the HadISST2 (Met Office Hadley Center). While the ERA-20CM is unable to reproduce the actual synoptic conditions, it is designed to offer a realistic statistical representation of the past climate, spanning the period 1899-2010. In this study we investigate global trends in significant wave height and marine wind speed based on ERA-20CM, using monthly mean data, upper percentiles and monthly/annual maxima. The aim of the study is to assess the quality of the trends and how these estimates are affected by different SST and SIC. Global trends are compared against corresponding estimates obtained with ERA-Interim (1979-2009), but also crosschecked against ERA-20C - an ECMWF pilot reanalysis of the 20th-century, known to most trustworthy in the Northern Hemisphere extratropics. Over the period 1900-2009, the 10 member ensemble yields trends mainly within +/- 5% per century. However, significant trends of opposite signs are found locally. Certain areas, like the eastern equatorial Pacific, highly affected by the El Niño Southern Oscillation, show stronger trends. In general, trends based on statistical quantities further into the tail of the distribution are found less reliable.

Hydrological Retrospective of floods and droughts: Case study in the Amazon

NASA Astrophysics Data System (ADS)

Wongchuig Correa, Sly; Cauduro Dias de Paiva, Rodrigo; Carlo Espinoza Villar, Jhan; Collischonn, Walter

2017-04-01

Recent studies have reported an increase in intensity and frequency of hydrological extreme events in many regions of the Amazon basin over last decades, these events such as seasonal floods and droughts have originated a significant impact in human and natural systems. Recently, methodologies such as climatic reanalysis are being developed in order to create a coherent register of climatic systems, thus taking this notion, this research efforts to produce a methodology called Hydrological Retrospective (HR), that essentially simulate large rainfall datasets over hydrological models in order to develop a record over past hydrology, enabling the analysis of past floods and droughts. We developed our methodology on the Amazon basin, thus we used eight large precipitation datasets (more than 30 years) through a large scale hydrological and hydrodynamic model (MGB-IPH), after that HR products were validated against several in situ discharge gauges dispersed throughout Amazon basin, given focus in maximum and minimum events. For better HR results according performance metrics, we performed a forecast skill of HR to detect floods and droughts considering in-situ observations. Furthermore, statistical temporal series trend was performed for intensity of seasonal floods and drought in the whole Amazon basin. Results indicate that better HR represented well most past extreme events registered by in-situ observed data and also showed coherent with many events cited by literature, thus we consider viable to use some large precipitation datasets as climatic reanalysis mainly based on land surface component and datasets based in merged products for represent past regional hydrology and seasonal hydrological extreme events. On the other hand, an increase trend of intensity was realized for maximum annual discharges (related to floods) in north-western regions and for minimum annual discharges (related to drought) in central-south regions of the Amazon basin, these features were previously detected by other researches. In the whole basin, we estimated an upward trend of maximum annual discharges at Amazon River. In order to estimate better future hydrological behavior and their impacts on the society, HR could be used as a methodology to understand past extreme events occurrence in many places considering the global coverage of rainfall datasets.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Observing Recent Changes in the Large-Scale Arctic Energy Budget

NASA Astrophysics Data System (ADS)

Porter, D. F.; Serreze, M.; Cassano, J.

2008-12-01

Changes in the large-scale energy budget of the Arctic are examined using a variety of next-generation reanalysis and observational data. An effort is made to construct a best-guess of the current arctic energy budget using a variety of atmospheric data. For the period of 2000-2005, monthly means from the Clouds and the Earth's Radiant Energy System (CERES) data represents the current most-reliable top of atmosphere radiation budget. The remaining components of the energy budget system in the arctic polar cap (defined as 70 degrees North latitude circle), comprising of the vertically-integrated storage and horizontal transports of energy, and net heat transfers between the atmosphere and the subsurface column, are diagnosed using the Japanese 25-year Reanalysis Project (JRA-25) and the NCEP/NCAR Reanalysis (NRA). The as then record-setting minimum sea-ice extent during the 2005 melt season is used as a marker of recent changes occurring in the arctic climate system. However, changes in each reanalysis differs than the satellite observations. In one example, when compared to the 2000-2005 climatology, CERES shows a shift in the peak TOA radiation from July to June in 2005, a change that is absent in the reanalyses and directly attributable to the early and pronounced albedo reduction. An earlier peak in TOA radiation can strongly modulate the flux energy convergence from lower latitudes through circulation changes. Here, the energy budget framework provides a simplified view of the pathway through which changes of key component parings occur.

A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

Effect of Impingement Angle on landfalling Atmospheric River precipitation efficiency

NASA Astrophysics Data System (ADS)

Mehran, A.; Cao, Q.; Wang, K.; Cannon, F.; Ralph, M.; Lettenmaier, D. P.

2017-12-01

Atmospheric Rivers (ARs) along the western coast of North America in wintertime are associated with heavy winter precipitation and most flood events. ARs are narrow, elongated, synoptic jets of water vapor that transport moisture from the eastern Pacific to North Pacific coast of North America. Furthermore, the lowest levels of the atmosphere account for almost 75% of the water vapor transport through these rivers. The combination of high integrated water vapor in AR events and strong upslope winds results in heavy orographic precipitation in regions where the narrow AR jets make landfall. We analyzed 19 years (1997 2015) of landfalling ARs over a transect along the U.S. West Coast consisting of two river basins from coastal Washington and Northern California (Chehalis basin and the Russian River basin) to highlight the impingement angle impact on precipitation rainout efficiency. We have studied water vapor data from Climate Forecast System reanalysis (CFSR) on AR dates to calculate the impingement angle and associated total amount of water vapor. Rainout efficiency is defined and calculated as the ratio of total amount of water vapor that has precipitated over each basin. Our results show that extreme AR events which impingement angle is orthogonal to basin exposure, have greater rainout efficiency.

Approximate Stokes Drift Profiles and their use in Ocean Modelling

NASA Astrophysics Data System (ADS)

Breivik, Oyvind; Bidlot, Jea-Raymond; Janssen, Peter A. E. M.; Mogensen, Kristian

2016-04-01

Deep-water approximations to the Stokes drift velocity profile are explored as alternatives to the monochromatic profile. The alternative profiles investigated rely on the same two quantities required for the monochromatic profile, viz the Stokes transport and the surface Stokes drift velocity. Comparisons against parametric spectra and profiles under wave spectra from the ERA-Interim reanalysis and buoy observations reveal much better agreement than the monochromatic profile even for complex sea states. That the profiles give a closer match and a more correct shear has implications for ocean circulation models since the Coriolis-Stokes force depends on the magnitude and direction of the Stokes drift profile and Langmuir turbulence parameterizations depend sensitively on the shear of the profile. Of the two Stokes drift profiles explored here, the profile based on the Phillips spectrum is by far the best. In particular, the shear near the surface is almost identical to that influenced by the f-5 tail of spectral wave models. The NEMO general circulation ocean model was recently extended to incorporate the Stokes-Coriolis force along with two other wave-related effects. The ECWMF coupled atmosphere-wave-ocean ensemble forecast system now includes these wave effects in the ocean model component (NEMO).

Modulation of Heavy Rainfall in the Middle East and North Africa by Madden-Julian Oscillation Using High Resolution Atmospheric General Circulation Model

NASA Astrophysics Data System (ADS)

Deng, L.; Stenchikov, G. L.; McCabe, M. F.; Bangalath, H. K.

2014-12-01

Recently, the modulation of subtropical rainfall by the dominant tropical intraseasonal signal of the Madden-Julian Oscillation (MJO), has been explored through the discussion of the MJO-convection-induced Kelvin and Rossby wave related teleconnection patterns. Our study focuses on characterizing the modulation of heavy rainfall in the Middle East and North Africa (MENA) region by the MJO, using the Geophysical Fluid Dynamics Laboratory (GFDL) global High Resolution Atmospheric Model (HIRAM) simulations (25-km; 1979-2012) and a combination of available atmospheric products from satellite, in-situ and reanalysis data. The observed Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and the simulated SST from GFDL's global coupled carbon-climate Earth System Models (ESM2M) are employed in HIRAM to investigate the sensitivity of the simulated heavy rainfall and MJO to SST. The future trend of the extreme rainfalls and their links to the MJO response to climate change are examined using HIRAM simulations of 2012-2050 with the RCP4.5 and RCP 8.5 scenarios to advance the possibility of characterization and forecasting of future extreme rainfall events in the MENA region.

Remote Sensing the Thermal and Humidity Structure of the Earth's Atmosphere Using the GPS Radio Occultation Technique: Applications in Climate Studies

NASA Astrophysics Data System (ADS)

Vergados, P.; Mannucci, A. J.; Ao, C. O.; Verkhoglyadova, O. P.; Iijima, B.

2017-12-01

This presentation introduces the fundamentals of the Global Positioning System radio occultation (GPS RO) remote sensing technique in retrieving atmospheric temperature and humidity information and presents the use of these observations in climate research. Our objective is to demonstrate and establish the GPS RO remote sensing technique as a complementary data set to existing state-of-the-art space-based platforms for climate studies. We show how GPS RO measurements at 1.2-1.6 GHz frequency band can be used to infer the upper tropospheric water vapor and temperature feedbacks and we present a decade-long specific humidity (SH) record from January 2007 until December 2015. We cross-compare the GPS RO-estimated climate feedbacks and the SH long-record with independent data sets from the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim), and the Atmospheric Infrared Sounder (AIRS) instrument. These cross-comparisons serve as a performance guide for the GPS-RO observations with respect to other data sets by providing an independent measure of climate feedbacks and humidity short-term trends.

Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

2015-12-01

Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

Strategic system development toward biofuel, desertification, and crop production monitoring in continental scales using satellite-based photosynthesis models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2013-10-01

The author regards fundamental root functions as underpinning photosynthesis activities by vegetation and as affecting environmental issues, grain production, and desertification. This paper describes the present development of monitoring and near real-time forecasting of environmental projects and crop production by approaching established operational monitoring step-by-step. The author has been developing a thematic monitoring structure (named RSEM system) which stands on satellite-based photosynthesis models over several continents for operational supports in environmental fields mentioned above. Validation methods stand not on FLUXNET but on carbon partitioning validation (CPV). The models demand continuing parameterization. The entire frame system has been built using Reanalysis meteorological data, but model accuracy remains insufficient except for that of paddy rice. The author shall accomplish the system that incorporates global environmental forces. Regarding crop production applications, industrialization in developing countries achieved through direct investment by economically developed nations raises their income, resulting in increased food demand. Last year, China began to import rice as it had in the past with grains of maize, wheat, and soybeans. Important agro-potential countries make efforts to cultivate new crop lands in South America, Africa, and Eastern Europe. Trends toward less food sustainability and stability are continuing, with exacerbation by rapid social and climate changes. Operational monitoring of carbon sequestration by herbaceous and bore plants converges with efforts at bio-energy, crop production monitoring, and socio-environmental projects such as CDM A/R, combating desertification, and bio-diversity.

Identification of Climatic Factors Affecting the Epidemiology of Human West Nile Virus Infections in Northern Greece.

PubMed

Stilianakis, Nikolaos I; Syrris, Vasileios; Petroliagkis, Thomas; Pärt, Peeter; Gewehr, Sandra; Kalaitzopoulou, Stella; Mourelatos, Spiros; Baka, Agoritsa; Pervanidou, Danai; Vontas, John; Hadjichristodoulou, Christos

2016-01-01

Climate can affect the geographic and seasonal patterns of vector-borne disease incidence such as West Nile Virus (WNV) infections. We explore the association between climatic factors and the occurrence of West Nile fever (WNF) or West Nile neuro-invasive disease (WNND) in humans in Northern Greece over the years 2010-2014. Time series over a period of 30 years (1979-2008) of climatic data of air temperature, relative humidity, soil temperature, volumetric soil water content, wind speed, and precipitation representing average climate were obtained utilising the ECMWF's (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-Interim) system allowing for a homogeneous set of data in time and space. We analysed data of reported human cases of WNF/WNND and Culex mosquitoes in Northern Greece. Quantitative assessment resulted in identifying associations between the above climatic variables and reported human cases of WNF/WNND. A substantial fraction of the cases was linked to the upper percentiles of the distribution of air and soil temperature for the period 1979-2008 and the lower percentiles of relative humidity and soil water content. A statistically relevant relationship between the mean weekly value climatic anomalies of wind speed (negative association), relative humidity (negative association) and air temperature (positive association) over 30 years, and reported human cases of WNF/WNND during the period 2010-2014 could be shown. A negative association between the presence of WNV infected Culex mosquitoes and wind speed could be identified. The statistically significant associations could also be confirmed for the week the WNF/WNND human cases appear and when a time lag of up to three weeks was considered. Similar statistically significant associations were identified with the weekly anomalies of the maximum and minimum values of the above climatic factors. Utilising the ERA-Interim re-analysis methodology it could be shown that besides air temperature, climatic factors such as soil temperature, relative humidity, soil water content and wind speed may affect the epidemiology of WNV.

Decadal variability of the position and strength of the South Atlantic Convergence Zone and its relationship to precipitation variability and extremes over Southeastern Brazil

NASA Astrophysics Data System (ADS)

Zilli, M. T.; Carvalho, L. V.; Lintner, B. R.

2016-12-01

The South Atlantic Convergence Zone (SACZ) is a diagonally oriented zone of low-level convergence, convective cloudiness, and rainfall originating over South America and extending to the southeast over the Atlantic Ocean. The objective of this study is to investigate the role of variability in the position and strength of the SACZ in causing precipitation variability and extremes over the southeastern Brazilian coast (SE). To that end, we perform Empirical Orthogonal Function (EOF) analysis of total summer (DJF) precipitation from 1979 to 2013, using the National Center for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), at 0.5° spatial resolution. The first mode (EOF-1) explains 22.7% of total variance and is characterized by a dipole-like structure, with opposite signs over central South America and over central South Atlantic along the northern margin of the SACZ. The time-coefficient or principal component of EOF-1 indicates a transition from a predominantly negative phase over 1999 to 2005 to a predominantly positive phase after 2006. The positive phase is associated with an increase in total precipitation over the continent and a reduction over the central South Atlantic, along the northern margin of the SACZ. These results provide evidence of the poleward shift of the SACZ and weakening of convergence along its northern margin over the past decade, consistent with the observed recent trends in extreme precipitation over SE. Compositing reanalysis fields with respect to the PC of EOF-1 suggests changes in moisture availability and circulation that could have affected precipitation regimes over SE. In particular, an increase in available precipitable water may have enhanced convective activity over the southern portion of SE Brazil, whereas the weakening of the northerly winds may be responsible for the weakening of convergence over the northern flank of the SACZ, inhibiting convection in this region.

Extracting Independent Local Oscillatory Geophysical Signals by Geodetic Tropospheric Delay

NASA Technical Reports Server (NTRS)

Botai, O. J.; Combrinck, L.; Sivakumar, V.; Schuh, H.; Bohm, J.

2010-01-01

Zenith Tropospheric Delay (ZTD) due to water vapor derived from space geodetic techniques and numerical weather prediction simulated-reanalysis data exhibits non-linear and non-stationary properties akin to those in the crucial geophysical signals of interest to the research community. These time series, once decomposed into additive (and stochastic) components, have information about the long term global change (the trend) and other interpretable (quasi-) periodic components such as seasonal cycles and noise. Such stochastic component(s) could be a function that exhibits at most one extremum within a data span or a monotonic function within a certain temporal span. In this contribution, we examine the use of the combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA): the EEMD-ICA algorithm to extract the independent local oscillatory stochastic components in the tropospheric delay derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) over six geodetic sites (HartRAO, Hobart26, Wettzell, Gilcreek, Westford, and Tsukub32). The proposed methodology allows independent geophysical processes to be extracted and assessed. Analysis of the quality index of the Independent Components (ICs) derived for each cluster of local oscillatory components (also called the Intrinsic Mode Functions (IMFs)) for all the geodetic stations considered in the study demonstrate that they are strongly site dependent. Such strong dependency seems to suggest that the localized geophysical signals embedded in the ZTD over the geodetic sites are not correlated. Further, from the viewpoint of non-linear dynamical systems, four geophysical signals the Quasi-Biennial Oscillation (QBO) index derived from the NCEP/NCAR reanalysis, the Southern Oscillation Index (SOI) anomaly from NCEP, the SIDC monthly Sun Spot Number (SSN), and the Length of Day (LoD) are linked to the extracted signal components from ZTD. Results from the synchronization analysis show that ZTD and the geophysical signals exhibit (albeit subtle) site dependent phase synchronization index.

Multi-decadal Hydrological Retrospective: Case study of Amazon floods and droughts

NASA Astrophysics Data System (ADS)

Wongchuig Correa, Sly; Paiva, Rodrigo Cauduro Dias de; Espinoza, Jhan Carlo; Collischonn, Walter

2017-06-01

Recently developed methodologies such as climate reanalysis make it possible to create a historical record of climate systems. This paper proposes a methodology called Hydrological Retrospective (HR), which essentially simulates large rainfall datasets, using this as input into hydrological models to develop a record of past hydrology, making it possible to analyze past floods and droughts. We developed a methodology for the Amazon basin, where studies have shown an increase in the intensity and frequency of hydrological extreme events in recent decades. We used eight large precipitation datasets (more than 30 years) as input for a large scale hydrological and hydrodynamic model (MGB-IPH). HR products were then validated against several in situ discharge gauges controlling the main Amazon sub-basins, focusing on maximum and minimum events. For the most accurate HR, based on performance metrics, we performed a forecast skill of HR to detect floods and droughts, comparing the results with in-situ observations. A statistical temporal series trend was performed for intensity of seasonal floods and droughts in the entire Amazon basin. Results indicate that HR could represent most past extreme events well, compared with in-situ observed data, and was consistent with many events reported in literature. Because of their flow duration, some minor regional events were not reported in literature but were captured by HR. To represent past regional hydrology and seasonal hydrological extreme events, we believe it is feasible to use some large precipitation datasets such as i) climate reanalysis, which is mainly based on a land surface component, and ii) datasets based on merged products. A significant upward trend in intensity was seen in maximum annual discharge (related to floods) in western and northwestern regions and for minimum annual discharge (related to droughts) in south and central-south regions of the Amazon basin. Because of the global coverage of rainfall datasets, this methodology can be transferred to other regions for better estimation of future hydrological behavior and its impact on society.

Seasonal Variability of Salt Transports in the Northern Indian Ocean

NASA Astrophysics Data System (ADS)

D'Addezio, J. M.; Bulusu, S.

2016-02-01

Due to limited observational data in the Indian Ocean compared to other regions of the global ocean, past work on the Northern Indian Ocean (NIO) has relied heavily upon model analysis to study the variability of regional salinity advection caused by the monsoon seasons. With the launch of the Soil Moisture and Ocean Salinity (SMOS) satellite in 2009 and the Aquarius SAC-D mission in 2011 (ended on June 7, 2011), remotely sensed, synoptic scale sea surface salinity (SSS) data is now readily available to study this dynamic region. The new observational data has allowed us to revisit the region to analyze seasonal variability of salinity advection in the NIO using several modeled products, the Aquarius and SMOS satellites, and Argo floats data. The model simulations include the Consortium for Estimating the Circulation and Climate of the Ocean (ECCO2), European Centre for Medium-Range Weather Forecasts - Ocean Reanalysis System 4 (ECMWF-ORSA4), Simple Ocean Data Assimilation (SODA) Reanalysis, and HYbrid Coordinate Ocean Model (HYCOM). Our analyses of salinity at the surface and at depths up to 200 m, surface salt transport in the top 5 m layer, and depth-integrated salt transports revealed different salinity processes in the NIO that are dominantly related to the semi-annual monsoons. Aquarius and SMOS prove useful tools for observing this dynamic region, and reveal some aspects of SSS that Argo cannot resolve. Meridional depth-integrated salt transports using the modeled products along 6°N revealed dominant advective processes from the surface towards near-bottom depths. Finally, a difference in subsurface salinity stratification causes many of the modeled products to incorrectly estimate the magnitude and seasonality of NIO barrier layer thickness (BLT) when compared to the Argo solution. This problem is also evident in model output from the Seychelles-Chagos Thermocline Ridge (SCTR), a region with strong air-sea teleconnections with the Arabian Sea.

National Centers for Environmental Prediction

Science.gov Websites

SYSTEM CFS CLIMATE FORECAST SYSTEM NAQFC NAQFC MODEL GEFS GLOBAL ENSEMBLE FORECAST SYSTEM HWRF HURRICANE WEATHER RESEARCH and FORECASTING HMON HMON - OPERATIONAL HURRICANE FORECASTING WAVEWATCH III WAVEWATCH III

Evaluation of reanalysis near-surface winds over northern Africa in Boreal summer

NASA Astrophysics Data System (ADS)

Engelstaedter, Sebastian; Washington, Richard

2014-05-01

The emission of dust from desert surfaces depends on the combined effects of surface properties such as surface roughness, soil moisture, soil texture and particle size (erodibility) and wind speed (erosivity). In order for dust cycle models to realistically simulate dust emissions for the right reasons, it is essential that erosivity and erodibility controlling factors are represented correctly. There has been a focus on improving dust emission schemes or input fields of soil distribution and texture even though it has been shown that the use of wind fields from different reanalysis datasets to drive the same model can result in significant differences in the dust emissions. Here we evaluate the representation of near-surface wind speed from three different reanalysis datasets (ERA-Interim, CFSR and MERRA) over the North African domain. Reanalysis 10m wind speeds are compared with observations from SYNOP and METAR reports available from the UK Meteorological Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Dataset. We compare 6-hourly observations of 10m wind speed between 1 January 1989 and 31 December 2009 from more the 500 surface stations with the corresponding reanalysis values. A station data based mean wind speed climatology for North Africa is presented. Overall, the representation of 10m winds is relatively poor in all three reanalysis datasets with stations in the northern parts of the Sahara still being better simulated (correlation coefficients ~ 0.5) than stations in the Sahel (correlation coefficients < 0.3) which points at the reanalyses not being able to realistically capture the Sahel dynamics systems. All three reanalyses have a systematic bias towards overestimating wind speed below 3-4 m/s and underestimating wind speed above 4 m/s. This bias becomes larger with increasing wind speed but is independent of the time of day. For instance, 14 m/s observed wind speeds are underestimated on average by 6 m/s in the ERA-Interim reanalysis. Given the cubic relationship between wind speed and dust emission this large underestimation is expected to significantly impact the simulation of dust emissions. A negative relationship between observed and ERA-Interim wind speed is found for winds above 14 m/s indicating that high wind speed generating processes are not well (if at all) represented in the model.

A robust scientific workflow for assessing fire danger levels using open-source software

NASA Astrophysics Data System (ADS)

Vitolo, Claudia; Di Giuseppe, Francesca; Smith, Paul

2017-04-01

Modelling forest fires is theoretically and computationally challenging because it involves the use of a wide variety of information, in large volumes and affected by high uncertainties. In-situ observations of wildfire, for instance, are highly sparse and need to be complemented by remotely sensed data measuring biomass burning to achieve homogeneous coverage at global scale. Fire models use weather reanalysis products to measure energy release and rate of spread but can only assess the potential predictability of fire danger as the actual ignition is due to human behaviour and, therefore, very unpredictable. Lastly, fire forecasting systems rely on weather forecasts to extend the advance warning but are currently calibrated using fire danger thresholds that are defined at global scale and do not take into account the spatial variability of fuel availability. As a consequence, uncertainties sharply increase cascading from the observational to the modelling stage and they might be further inflated by non-reproducible analyses. Although uncertainties in observations will only decrease with technological advances over the next decades, the other uncertainties (i.e. generated during modelling and post-processing) can already be addressed by developing transparent and reproducible analysis workflows, even more if implemented within open-source initiatives. This is because reproducible workflows aim to streamline the processing task as they present ready-made solutions to handle and manipulate complex and heterogeneous datasets. Also, opening the code to the scrutiny of other experts increases the chances to implement more robust solutions and avoids duplication of efforts. In this work we present our contribution to the forest fire modelling community: an open-source tool called "caliver" for the calibration and verification of forest fire model results. This tool is developed in the R programming language and publicly available under an open license. We will present the caliver R package, illustrate the main functionalities and show the results of our preliminary experiments calculating fire danger thresholds for various regions on Earth. We will compare these with the existing global thresholds and, lastly, demonstrate how these newly-calculated regional thresholds can lead to improved calibration of fire forecast models in an operational setting.

Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

NASA Astrophysics Data System (ADS)

Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

2014-05-01

This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

NASA Astrophysics Data System (ADS)

Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

2014-11-01

This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

Impact of DYNAMO observations on NASA GEOS-5 reanalyses and the representation of MJO initiation

NASA Astrophysics Data System (ADS)

Achuthavarier, D.; Wang, H.; Schubert, S. D.; Sienkiewicz, M.

2017-01-01

This study examines the impact of the Dynamics of the Madden-Julian Oscillation (DYNAMO) campaign in situ observations on NASA Goddard Earth Observing System version 5 (GEOS-5) reanalyses and the improvements gained thereby in the representation of the Madden-Julian Oscillation (MJO) initiation processes. To this end, we produced a global, high-resolution (1/4° spatially) reanalysis that assimilates the level-4, quality-controlled DYNAMO upper air soundings from about 87 stations in the equatorial Indian Ocean region along with a companion data-denied control reanalysis. The DYNAMO reanalysis produces a more realistic vertical structure of the temperature and moisture in the central tropical Indian Ocean by correcting the model biases, namely, the cold and dry biases in the lower troposphere and warm bias in the upper troposphere. The reanalysis horizontal winds are substantially improved, in that, the westerly acceleration and vertical shear of the zonal wind are enhanced. The DYNAMO reanalysis shows enhanced low-level diabatic heating, moisture anomalies and vertical velocity during the MJO initiation. Due to the warmer lower troposphere, the deep convection is invigorated, which is evident in convective cloud fraction. The GEOS-5 atmospheric general circulation model (AGCM) employed in the reanalysis is overall successful in assimilating the additional DYNAMO observations, except for an erroneous model response for medium rain rates, between 700 and 600 hPa, reminiscent of a bias in earlier versions of the AGCM. The moist heating profile shows a sharp decrease there due to the excessive convective rain re-evaporation, which is partly offset by the temperature increment produced by the analysis.

The FALL3D Ash Cloud Dispersion Model and its Implementation at the Buenos Aires VAAC

NASA Astrophysics Data System (ADS)

Folch, A.; Suaya, M.; Costa, A.; Viramonte, J.

2009-12-01

Airborne volcanic ash and aerosols threat aerial navigation and affect the quality of air at medium to large distances downwind from the volcano. Airplane re-routing and airport disruption carry important socioeconomic consequences at regional and national levels. Models to forecast volcanic ash clouds constitute, together with satellite imagery, a valuable predictive tool during a crisis. FALL3D is an Eulerian ash cloud dispersion model based on the advection-diffusion-sedimentation equation. The model runs at any scale, from regional to global. The dispersion model is off-line coupled with global (e.g. GFS, NMM-b) and mesoscalar (e.g. NMM-b, WRF, ETA) meteorological models and with re-analysis datasets. FALL3D has been recently installed at the Buenos Aires VAAC, depending on the Argentinean National Meteorological Service (SMN). In this presentation we summarize the characteristics of the model and its implementation at the VAAC, including the different domains, the meteorological forecast inputs (ETA or GFS) and the scenarios assumed for some critical volcanoes (Chaitén, Llaima, Lascar, etc.). Pre-defined scenarios are necessary to give an early first order prediction when data is poor or unavailable. This is particularly critical in Central Andes, were most active volcanoes are located in remote areas with poor or inexistent monitoring.

Current State and Recent Changes in the Arctic Ocean from the HYCOM-NCODA Global Ocean and Sea Ice Prediction System

NASA Astrophysics Data System (ADS)

Dukhovskoy, D. S.; Chassignet, E. P.; Hogan, P. J.; Metzger, E. J.; Posey, P.; Smedstad, O. M.; Stefanova, L. B.; Wallcraft, A. J.

2016-12-01

The great potential of numerical models to provide a high-resolution continuous picture of the environmental characteristics of the Arctic system is related to the problem of reliability and accuracy of the simulations. Recent Arctic Ocean model intercomparison projects have identified substantial disagreements in water mass distribution and circulation among the models over the last two decades. In situ and satellite observations cannot yield enough continuous in time and space information to interpret the observed changes in the Arctic system. Observations combined with Arctic Ocean models via data assimilation provide perhaps the most complete knowledge about the state of the Arctic system. We use outputs from the US Navy Global Ocean Forecast System (20-year reanalysis + analysis) to investigate several hypotheses that have been put forward regarding the current state and recent changes in the Arctic Ocean. The system is based on the 0.08-degree HYbrid Coordinate Ocean Model (HYCOM) and can be run with two-way coupling to the Los Alamos Community Ice CodE (CICE) or with an energy-loan ice model. Observations are assimilated by the Navy Coupled Ocean Data Assimilation (NCODA) algorithm. HYCOM temperature and salinity fields are shown to be in good agreement with observational data in the Arctic and North Atlantic. The model reproduces changes in the freshwater budget in the Arctic as reported in other studies. The modeled freshwater fluxes between the Arctic Ocean and the North Atlantic are analyzed to document and discuss the interaction between the two regions over the last two decades.

The eSurge-Venice project: how satellite data can improve the storm surge forecasting in the northern Adriatic Sea

NASA Astrophysics Data System (ADS)

Zecchetto, Stefano; Vignudelli, Stefano; Donlon, Craig; De Biasio, Francesco; Della Valle, Antonio; Umgiesser, Georg; Bajo, Marco

The Data User Element (DUE) program of the European Space Agency (ESA) is funding two projects (eSurge and eSurge-Venice) aimed to demonstrate the improvement of the storm surge forecasting through the use of Earth Observation (EO) data. eSurge-Venice (http://www.esurge-venice.eu/), is specifically focused on the Gulf of Venice, northern Adriatic Sea. The project objectives are: a) Select a number of Storm Surge Events occurred in the Venice lagoon since 1999; b) Provide the available satellite EO data related to the Storm Surge Events, mainly satellite winds and altimeter data, as well as all the available in-situ data and model forecasts; c) Provide a demonstration Near Real Time service (eSurge-Venice live) of EO data products and services in support of operational and experimental forecasting and warning services; d) Run a number of re-analysis cases, both for historical and contemporary storm surge events, to demonstrate the usefulness of EO data. Present storm surge models use atmospheric model wind fields as forcing. These are know to underestimate the wind in small basins like the Adriatic Sea (~1000 km by 300 km), where the orography plays an important role in shaping the winds. Therefore there is the need to verify and tune the atmospheric model wind fields used in the storm surge modeling, an activity which can easily done using satellite scatterometer winds. The project is now in the middle of his life, and promising preliminary results have been achieved using satellite scatterometer wind data to forge the atmospheric model wind fields forcing the storm surge model. This contribution will present the methodology adopted to tune the model wind fields according to the bias with scatterometer winds and the improvements induced in the storm surge model hindcast.

Who was the agent? The neural correlates of reanalysis processes during sentence comprehension.

PubMed

Hirotani, Masako; Makuuchi, Michiru; Rüschemeyer, Shirley-Ann; Friederici, Angela D

2011-11-01

Sentence comprehension is a complex process. Besides identifying the meaning of each word and processing the syntactic structure of a sentence, it requires the computation of thematic information, that is, information about who did what to whom. The present fMRI study investigated the neural basis for thematic reanalysis (reanalysis of the thematic roles initially assigned to noun phrases in a sentence) and its interplay with syntactic reanalysis (reanalysis of the underlying syntactic structure originally constructed for a sentence). Thematic reanalysis recruited a network consisting of Broca's area, that is, the left pars triangularis (LPT), and the left posterior superior temporal gyrus, whereas only LPT showed greater sensitivity to syntactic reanalysis. These data provide direct evidence for a functional neuroanatomical basis for two linguistically motivated reanalysis processes during sentence comprehension. Copyright © 2010 Wiley-Liss, Inc.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: Dual-Resolution Implementation and Testing Results

NASA Astrophysics Data System (ADS)

Pan, Yujie; Xue, Ming; Zhu, Kefeng; Wang, Mingjun

2018-05-01

A dual-resolution (DR) version of a regional ensemble Kalman filter (EnKF)-3D ensemble variational (3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution (HR) deterministic background forecast with lower-resolution (LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/˜13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation (GSI) 3D variational (3DVar) analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar. Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

On the physical air-sea fluxes for climate modeling

NASA Astrophysics Data System (ADS)

Bonekamp, J. G.

2001-02-01

At the sea surface, the atmosphere and the ocean exchange momentum, heat and freshwater. Mechanisms for the exchange are wind stress, turbulent mixing, radiation, evaporation and precipitation. These surface fluxes are characterized by a large spatial and temporal variability and play an important role in not only the mean atmospheric and oceanic circulation, but also in the generation and sustainment of coupled climate fluctuations such as the El Niño/La Niña phenomenon. Therefore, a good knowledge of air-sea fluxes is required for the understanding and prediction of climate changes. As part of long-term comprehensive atmospheric reanalyses with `Numerical Weather Prediction/Data assimilation' systems, data sets of global air-sea fluxes are generated. A good example is the 15-year atmospheric reanalysis of the European Centre for Medium--Range Weather Forecasts (ECMWF). Air-sea flux data sets from these reanalyses are very beneficial for climate research, because they combine a good spatial and temporal coverage with a homogeneous and consistent method of calculation. However, atmospheric reanalyses are still imperfect sources of flux information due to shortcomings in model variables, model parameterizations, assimilation methods, sampling of observations, and quality of observations. Therefore, assessments of the errors and the usefulness of air-sea flux data sets from atmospheric (re-)analyses are relevant contributions to the quantitative study of climate variability. Currently, much research is aimed at assessing the quality and usefulness of the reanalysed air-sea fluxes. Work in this thesis intends to contribute to this assessment. In particular, it attempts to answer three relevant questions. The first question is: What is the best parameterization of the momentum flux? A comparison is made of the wind stress parameterization of the ERA15 reanalysis, the currently generated ERA40 reanalysis and the wind stress measurements over the open ocean. The comparison reveals some clear differences in the mean drag coefficient. In addition, this study has indicated that progress has been made from the ERA15 to the ERA40 reanalyses by replacing the model parameterization with a constant Charnock parameter with one which depends on the sea state. The second research question is whether comparison of the response of an ocean model with ocean observations can be exploited to assess the quality of air-sea fluxes of the ERA15 reanalysis. To answer this question in a systematic way an inverse modeling approach is adopted using a four-dimensional variational data assimilation (4DVAR) scheme. Firstly, the functioning of the 4DVAR system is demonstrated from identical twin experiments. These experiments reveal that in the equatorial Pacific, a large reduction in wind-stress and upper-ocean temperature misfits can be achieved using an assimilation time window of eight weeks. It is concluded that the usefulness of inverse ocean modeling technique for global surface flux assessment is limited. The main merit of the developed ocean 4DVAR scheme will be to diagnose errors in the ocean analyses of the ocean model. The last research question is: are the ERA15 fluxes useful for the study of regional patterns of climate variability? The climate mode of consideration is the Antarctic Circumpolar Wave. This study stresses the importance to have the right climatological forcing conditions to assess time scales of climate variability and it confirms the usefulness of ERA15 air-sea fluxes as ocean model forcing fields to study climate variability on the interannual time scale.

Physically Based Mountain Hydrological Modelling using Reanalysis Data in Patagonia

NASA Astrophysics Data System (ADS)

Krogh, S.; Pomeroy, J. W.; McPhee, J. P.

2013-05-01

Remote regions in South America are often characterized by insufficient observations of meteorology for robust hydrological model operation. Yet water resources must be quantified, understood and predicted in order to develop effective water management policies. Here, we developed a physically based hydrological model for a major river in Patagonia using the modular Cold Regions Hydrological Modelling Platform (CRHM) in order to better understand hydrological processes leading to streamflow generation in this remote region. The Baker River -with the largest mean annual streamflow in Chile-, drains snowy mountains, glaciers, wet forests, peat and semi-arid pampas into a large lake. Meteorology over the basin is poorly monitored in that there are no high elevation weather stations and stations at low elevations are sparsely distributed, only measure temperature and rainfall and are poorly maintained. Streamflow in the basin is gauged at several points where there are high quality hydrometric stations. In order to quantify the impact of meteorological data scarcity on prediction, two additional data sources were used: the ERA-Interim (ECMWF Re-analyses) and CFSR (Climate Forecast System Reanalysis) atmospheric reanalyses. Precipitation temporal distribution and magnitude from the models and observations were compared and the reanalysis data was found to have about three times the number of days with precipitation than the observations did. Better synchronization between measured peak streamflows and modeled precipitation was found compared to observed precipitation. These differences are attributed to: (i) lack of any snowfall observations (so precipitation records does not consider snowfall events) and (ii) available rainfall observations are all located at low altitude (<500 m a.s.l), and miss the occurrence of high altitude precipitation events. CRHM parameterization was undertaken by using local physiographic and vegetation characteristics where available and transferring locally unknown hydrological process parameters from cold regions mountain environments in Canada. Some soil moisture parameters were calibrated from streamflow observations. Model performance was estimated through comparison with observed streamflow records. Simulations using observed precipitation had negligible representativeness of streamflow (Nash-Sutcliffe coefficient, NS ≈ 0.2), while those using any of the two reanalyses as forcing data had reasonable model performance (NS ≈ 0.7). In spite of the better spatial resolution of the CFSR, the ability to simulate streamflow were not significantly different using either CFSR or ERA-Interim. The modeled water balance shows that snowfall is about 30% of the total precipitation input, but snowmelt superficial runoff comprises about 10% of total runoff. About 75% of all precipitation is infiltrated, and approximately 15% of the losses are attributed to evapotranspiration from soil and lake evaporation.

Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

NASA Technical Reports Server (NTRS)

Buchard, V.; Da Silva, A.

2016-01-01

MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Arctic atmospheric preconditioning: do not rule out shortwave radiation just yet

NASA Astrophysics Data System (ADS)

Sedlar, J.

2017-12-01

Springtime atmospheric preconditioning of Arctic sea ice for enhanced or buffered sea ice melt during the subsequent melt year has received considerable research focus in recent years. A general consensus points to enhanced poleward atmospheric transport of moisture and heat during spring, effectively increasing the emission of longwave radiation to the surface. Studies have essentially ruled out the role of shortwave radiation as an effective preconditioning mechanism because of the relatively weak incident solar radiation and high surface albedo from sea ice and snow during spring. These conclusions, however, are derived primarily from atmospheric reanalysis data, which may not always represent an accurate depiction of the Arctic climate system. Here, observations of top of atmosphere radiation from state of the art satellite sensors are examined and compared with reanalysis and climate model data to examine the differences in the spring radiative budget over the Arctic Ocean for years with extreme low/high ice extent at the end of the ice melt season (September). Distinct biases are observed between satellite-based measurements and reanalysis/models, particularly for the amount of shortwave radiation trapped (warming effect) within the Arctic climate system during spring months. A connection between the differences in reanalysis/model surface albedo representation and the albedo observed by satellite is discussed. These results suggest that shortwave radiation should not be overlooked as a significant contributing mechanism to springtime Arctic atmospheric preconditioning.

A WRF sensitivity study for summer ozone and winter PM events in California

NASA Astrophysics Data System (ADS)

Zhao, Z.; Chen, J.; Mahmud, A.; Di, P.; Avise, J.; DaMassa, J.; Kaduwela, A. P.

2014-12-01

Elevated summer ozone and winter PM frequently occur in the San Joaquin Valley (SJV) and the South Coast Air Basin (SCAB) in California. Meteorological conditions, such as wind, temperature and planetary boundary layer height (PBLH) play crucial roles in these air pollution events. Therefore, accurate representation of these fields from a meteorological model is necessary to successfully reproduce these air pollution events in subsequent air quality model simulations. California's complex terrain and land-sea interface can make it challenging for meteorological models to replicate the atmospheric conditions over the SJV and SCAB during extreme pollution events. In this study, the performance of the Weather Research and Forecasting Model (WRF) over these two regions for a summer month (July 2012) and a winter month (January 2013) is evaluated with different model configurations and forcing. Different land surface schemes (Pleim-Xiu vs. hybrid scheme), the application of observational and soil nudging, two SST datasets (the Global Ocean Data Assimilation Experiment (GODAE) SST vs. the default SST from North American Regional Reanalysis (NARR) reanalysis), and two land use datasets (the National Land Cover Data (NLCD) 2006 40-category vs. USGS 24-category land use data) have been tested. Model evaluation will focus on both surface and vertical profiles for wind, temperature, relative humidity, as well as PBLH. Sensitivity of the Community Multi-scale Air Quality Model (CMAQ) results to different WRF configurations will also be presented and discussed.

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

NASA Astrophysics Data System (ADS)

Mehran, Ali; Clark, Elizabeth A.; Lettenmaier, Dennis P.

2017-11-01

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

Statistical Downscaling of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia

NASA Astrophysics Data System (ADS)

Kumar, Anikender; Rojas, Nestor

2015-04-01

Statistical downscaling is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). The fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model is used to simulate air quality over Bogota. Bogota is a tropical Andean megacity located over a high-altitude plateau in the middle of very complex terrain. The WRF-Chem model was adopted for simulating the hourly ozone concentrations. The computational domains were chosen of 120x120x32, 121x121x32 and 121x121x32 grid points with horizontal resolutions of 27, 9 and 3 km respectively. The model was initialized with real boundary conditions using NCAR-NCEP's Final Analysis (FNL) and a 1ox1o (~111 km x 111 km) resolution. Boundary conditions were updated every 6 hours using reanalysis data. The emission rates were obtained from global inventories, namely the REanalysis of the TROpospheric (RETRO) chemical composition and the Emission Database for Global Atmospheric Research (EDGAR). Multiple linear regression and artificial neural network techniques are used to downscale the model output at each monitoring stations. The results confirm that the statistically downscaled outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical downscaling of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.

Search for an astronomical site on the Arabian Peninsula: meteorological and climatological analyses

NASA Astrophysics Data System (ADS)

Sultan, A. H.; Graham, E.

The Arabian Peninsula is the richest in oil but the poorest in A A -Astronomy and Astrophysics- the largest telescope in the region doesn t exceed 45cm To promote A A education and research we propose that all the countries of the region work together to install an optical regional observatory telescope diameter 2 meters on an accessible summit somewhere within the mountains of the Arabian Peninsula The first step is to make a climatological and meteorological study of the highest summits of the region A preliminary study has revealed only one mountain peak above 3000 meters in Saudi Arabia one in Oman but more than thirty in Yemen Of all these summits we have narrowed the selection to six candidate sites on which we are performing detailed meteorological and climatological analyses Our database is composed mainly of Reanalysis datasets from the European Centre for Medium Range Weather Forecasting ECMWF and the National Center for Environmental Protection National Center for Atmospheric Research NCEP-NCAR Reanalysis datasets are reconstructions of all available past weather station data aeroplane sensor data weather balloon data weather ship data and satellite data from the 1950s onwards using sophisticated numerical weather prediction and data assimilation models This paper discusses ECMWF and NCEP-NCAR images of Arabian Peninsula for the following parameters at a monthly mean temporal resolution begin enumerate item Temperature variability at 700hPa item Precipitation item Geopotential height of the

The role of the tropical West Pacific in the extreme northern hemisphere winter of 2013/14

NASA Astrophysics Data System (ADS)

Watson, Peter; Weisheimer, Antje; Knight, Jeff; Palmer, Tim

2016-04-01

In the 2013/14 winter, the eastern USA was exceptionally cold, the Bering Strait region was exceptionally warm, California was in the midst of drought and the UK suffered severe flooding. It has been suggested that elevated SSTs in the tropical West Pacific (TWPAC) were partly to blame due to their producing a Rossby wavetrain that propagated into the extratropics. We find that seasonal forecasts with the tropical atmosphere relaxed towards a reanalysis give 2013/14 winter-mean anomalies with strong similarities to those observed in the Northern Hemisphere, indicating that low-latitude anomalies had a role in the development of the extremes. Relaxing just the TWPAC produces a strong wavetrain over the North Pacific and North America in January, but not in the winter-mean. This suggests that anomalies in this region alone had a large influence, but cannot explain the extremes through the whole winter. We also examine the response to applying the observed TWPAC SST anomalies in two atmospheric general circulation models. We find that this does produce winter-mean anomalies in the North Pacific and North America resembling those observed, but that the tropical forcing of Rossby waves due to the applied SST anomalies appears stronger than that in reanalysis, except in January. Therefore both experiments indicate that the TWPAC influence was important, but the true strength of the TWPAC influence is uncertain. None of the experiments indicate a strong systematic impact of the TWPAC anomalies on Europe.

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

High Resolution Nature Runs and the Big Data Challenge

NASA Technical Reports Server (NTRS)

Webster, W. Phillip; Duffy, Daniel Q.

2015-01-01

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

Some mean atmospheric characteristics for snowfall occurrences in southern Brazil

NASA Astrophysics Data System (ADS)

Mintegui, Jéssica Melo; Puhales, Franciano Scremin; Boiaski, Nathalie Tissot; Nascimento, Ernani de Lima; Anabor, Vagner

2018-01-01

Snowfall is considered a natural disaster in southern Brazil, where a little infrastructure exists up to prevent against the damage it induces, making snowfall forecast a matter of great interest in this region. The present article aims to describe the mean behavior of low, mid, and high atmospheric levels during snowfall occurrences in southern Brazil. Sea-level pressure (SLP), 1000-500 hPa atmospheric thickness, geopotential height at 500 hPa, and wind speed at 200 hPa have been analyzed. One hundred and ninety-six snowfall records from the conventional surface meteorological stations have been selected for the period from 1979 to 2015. The surface synoptic pattern associated with snowfall occurrences has been obtained from ERA-Interim reanalysis data with horizontal spatial resolution of 0.75° × 0.75° and temporal resolution of 12 h. SLP fields show a high-pressure transient system displacement from the Pacific Ocean to northeastern Argentina. In addition, it is possible to relate snowfall with displacement of a low-pressure system on the coast of southern Brazil. Thickness fields indicate shallow cold air mass intrusions one day before snowfall. Such a cold air continues moving towards low latitudes during consecutive snowfall days and it may be responsible for frost events in climatologically warm regions. Finally, mid and high atmospheric levels show an eastward propagating wave amplified by the Andes.

Data assimilation and model evaluation experiment datasets

NASA Technical Reports Server (NTRS)

Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

1994-01-01

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Spatial optimization of an ideal wind energy system as a response to the intermittency of renewable energies?

NASA Astrophysics Data System (ADS)

Lassonde, Sylvain; Boucher, Olivier; Breon, François-Marie; Tobin, Isabelle; Vautard, Robert

2016-04-01

The share of renewable energies in the mix of electricity production is increasing worldwide. This trend is driven by environmental and economic policies aiming at a reduction of greenhouse gas emissions and an improvement of energy security. It is expected to continue in the forthcoming years and decades. Electricity production from renewables is related to weather and climate factors such as the diurnal and seasonal cycles of sunlight and wind, but is also linked to variability on all time scales. The intermittency in the renewable electricity production (solar, wind power) could eventually hinder their future deployment. Intermittency is indeed a challenge as demand and supply of electricity need to be balanced at any time. This challenge can be addressed by the deployment of an overcapacity in power generation (from renewable and/or thermal sources), a large-scale energy storage system and/or improved management of the demand. The main goal of this study is to optimize a hypothetical renewable energy system at the French and European scales in order to investigate if spatial diversity of the production (here electricity from wind energy) could be a response to the intermittency. We use ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-interim meteorological reanalysis and meteorological fields from the Weather Research and Forecasts (WRF) model to estimate the potential for wind power generation. Electricity demand and production are provided by the French electricity network (RTE) at the scale of administrative regions for years 2013 and 2014. Firstly we will show how the simulated production of wind power compares against the measured production at the national and regional scale. Several modelling and bias correction methods of wind power production will be discussed. Secondly, we will present results from an optimization procedure that aims to minimize some measure of the intermittency of wind energy. For instance we estimate the optimal distribution between French regions (with or without cross-border inputs) that minimizes the impact of low-production periods computed in a running mean sense and its sensitivity to the period considered. We will also assess which meteorological situations are the most problematic over the 35-year ERA-interim climatology(1980-2015).

Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random dynamical models from time series," Phys. Rev. E, vol. 85, no. 3, p. 036216, 2012. [2] D. Mukhin, D. Kondrashov, E. Loskutov, A. Gavrilov, A. Feigin, and M. Ghil, "Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models," J. Clim., vol. 28, no. 5, pp. 1962-1976, 2015.

Dynamic analysis, transformation, dissemination and applications of scientific multidimensional data in ArcGIS Platform

NASA Astrophysics Data System (ADS)

Shrestha, S. R.; Collow, T. W.; Rose, B.

2016-12-01

Scientific datasets are generated from various sources and platforms but they are typically produced either by earth observation systems or by modelling systems. These are widely used for monitoring, simulating, or analyzing measurements that are associated with physical, chemical, and biological phenomena over the ocean, atmosphere, or land. A significant subset of scientific datasets stores values directly as rasters or in a form that can be rasterized. This is where a value exists at every cell in a regular grid spanning the spatial extent of the dataset. Government agencies like NOAA, NASA, EPA, USGS produces large volumes of near real-time, forecast, and historical data that drives climatological and meteorological studies, and underpins operations ranging from weather prediction to sea ice loss. Modern science is computationally intensive because of the availability of an enormous amount of scientific data, the adoption of data-driven analysis, and the need to share these dataset and research results with the public. ArcGIS as a platform is sophisticated and capable of handling such complex domain. We'll discuss constructs and capabilities applicable to multidimensional gridded data that can be conceptualized as a multivariate space-time cube. Building on the concept of a two-dimensional raster, a typical multidimensional raster dataset could contain several "slices" within the same spatial extent. We will share a case from the NOAA Climate Forecast Systems Reanalysis (CFSR) multidimensional data as an example of how large collections of rasters can be efficiently organized and managed through a data model within a geodatabase called "Mosaic dataset" and dynamically transformed and analyzed using raster functions. A raster function is a lightweight, raster-valued transformation defined over a mixed set of raster and scalar input. That means, just like any tool, you can provide a raster function with input parameters. It enables dynamic processing of only the data that's being displayed on the screen or requested by an application. We will present the dynamic processing and analysis of CFSR data using the chains of raster function and share it as dynamic multidimensional image service. This workflow and capabilities can be easily applied to any scientific data formats that are supported in mosaic dataset.

Evaluation of representativeness of near-surface winds in station measurements, global and regional reanalysis for Germany

NASA Astrophysics Data System (ADS)

Kaspar, Frank; Kaiser-Weiss, Andrea K.; Heene, Vera; Borsche, Michael; Keller, Jan

2015-04-01

Within the preparation activities for a European COPERNICUS Climate Change Service (C3S) several ongoing research projects analyse the potential of global and regional model-based climate reanalyses for applications. A user survey in the FP7-project CORE-CLIMAX revealed that surface wind (10 m) is among the most frequently used parameters of global reanalysis products. The FP7 project UERRA (Uncertainties in Ensembles of Regional Re-Analysis) has the focus on regional European reanalysis and the associated uncertainties, also from a user perspective. Especially in the field of renewable energy planning and production there is a need for climatological information across all spatial scales, i.e., from climatology at a certain site to the spatial scale of national or continental renewable energy production. Here, we focus on a comparison of wind measurements of the Germany's meteorological service (Deutscher Wetterdienst, DWD) with global reanalyses of ECWMF and a regional reanalysis for Europe based on DWD's NWP-model COSMO (performed by the Hans-Ertel-Center for Weather Research, University of Bonn). Reanalyses can provide valuable additional information on larger scale variability, e.g. multi-annual variation over Germany. However, changes in the observing system, model errors and biases have to be carefully considered. On the other hand, the ground-based observation networks partly suffer from change of the station distribution, changes in instrumentation, measurements procedures and quality control as well as local changes which might modify their spatial representativeness. All these effects might often been unknown or hard to characterize, although plenty of the meta-data information has been recorded for the German stations. One focus of the presentation will be the added-value of the regional reanalysis.

Multidecadal Changes in the UTLS Ozone from the MERRA-2 Reanalysis and the GMI Chemistry Model

NASA Technical Reports Server (NTRS)

Wargan, Krzysztof; Orbe, Clara; Pawson, Steven; Ziemke, Jerald R.; Oman, Luke; Olsen, Mark; Coy, Lawrence; Knowland, Emma

2018-01-01

Long-term changes of ozone in the UTLS (Upper Troposphere / Lower Stratosphere) reflect the response to decreases in the stratospheric concentrations of ozone-depleting substances as well as changes in the stratospheric circulation induced by climate change. To date, studies of UTLS ozone changes and variability have relied mainly on satellite and in-situ observations as well as chemistry-climate model simulations. By comparison, the potential of reanalysis ozone data remains relatively untapped. This is despite evidence from recent studies, including detailed analyses conducted under SPARC (Scalable Processor Architecture) Reanalysis Intercomparison Project (S-RIP), that demonstrate that stratospheric ozone fields from modern atmospheric reanalyses exhibit good agreement with independent data while delineating issues related to inhomogeneities in the assimilated observations. In this presentation, we will explore the possibility of inferring long-term geographically and vertically resolved behavior of the lower stratospheric (LS) ozone from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications -2) reanalysis after accounting for the few known discontinuities and gaps in its assimilated input data. This work builds upon previous studies that have documented excellent agreement between MERRA-2 ozone and ozonesonde observations in the LS. Of particular importance is a relatively good vertical resolution of MERRA-2 allowing precise separation of tropospheric and stratospheric ozone contents. We also compare the MERRA-2 LS ozone results with the recently completed 37-year simulation produced using Goddard Earth Observing System in "replay"� mode coupled with the GMI (Global Modeling Initiative) chemistry mechanism. Replay mode dynamically constrains the model with the MERRA-2 reanalysis winds, temperature, and pressure. We will emphasize the areas of agreement of the reanalysis and replay and interpret differences between them in the context of our increasing understanding of model transport driven by assimilated winds.

The Mediterranean Sea 1985-2007 re-analysis: validation results

NASA Astrophysics Data System (ADS)

Adani, Mario; Dobricic, Srdjan; Pinardi, Nadia

2010-05-01

Re-analyses are different from analyses because they are consistent for the whole period since the oceanic state estimates are produced without changes in the modelling assumptions and they are usually done with systems which are more advance then the available systems at the time of the observations collection. A fundamental part of a re-analysis system is the data assimilation scheme which minimizes the cost function penalizing the time-space misfits between the data and the numerical solutions, with the constraint of the model equations and their parameters. In this work we will compare ocean circulation estimates provided by pure simulation, a system in which the assimilation scheme is based on a sequential algorithm: Optimal Interpolation (OI) and a three-dimensional variational scheme (3dvar). The OGCM used in this work is based on OPA 8.1 code (Madec et al. 1998), which has been implemented in the Mediterranean Sea by Tonani et al.(2008). The model has 1/16th horizontal resolution and 71 unevenly spaced vertical levels. The present model formulation uses a realistic water flux with river runoffs which improves the realism of the simulation. One re-analysis is produced with the Reduced Order Optimal Interpolation (ROOI) (De Mey and Benkiran, 2002) and the other with OceanVar (Dobricic and Pinardi, 2008). The observational data sets assimilated for both reanalysis are: • the historical data archive of MedATLAS (Maillard et al., 2003) which contains vertical in situ profiles of temperature and salinity from bottles, XBT, MBT and CTD sensors • temperature and salinity profiles collected in the framework of MFSPP and MFSTEP projects • CLS along track satellite sea level anomaly data from ERS1, ERS2, Envisat, Topex/Poseidon, Jason1 satellites (Pujol and Larnicol,2005) Reanalyzed daily mean fields of Sea Surface Temperature (SST) from Medspiration (Marullo et al., 2007) and the Delayed-Time operational product of CNR-ISAC have been used to relax the model SST. The Mean Dynamic Topography of (Dobricic, 2005) has been used for both experiments. The model is forced with a combined dataset of ECMWF analysis when available and ERA-15. The precipitations are monthly mean climatology of the NCEP re-analysis (Kistler et.al 2001), the river runoff data are monthly mean climatology from the Global Runoff Data Centre (GRDC) and from Raicic (1996) for the minor Adriatic Sea rivers. The assimilation schemes help in reducing the spin up time of the model by acting as a forcing inside the water column. Both re-analyses show significantly better results then the simulation reducing both bias and root mean square error even though the structure of the error remains almost the same of the simulation: the largest error for tracers is confined in the thermocline especially in summer, highlighting a problem in the mixing parameterization; the majors error for SLA is confined in the most dynamically active areas. Satellite altimetry observations result in a fundamental dataset to constrain model solution and since its homogeneity in the sampling they permits a consistent assessment of the model behaviour along the years which it is not possible from in-situ observations whose sampling is extremely inhomogeneous both in time and space. This study describes the development of modelling and data assimilation tools for the production of re-analysis for the entire Mediterranean Sea. In order to carry out a re-analysis two major steps were undertaken in this work. In the first, the general circulation model was upgraded to have the correct air-sea water fluxes. In the second, two assimilation schemes, one new and the other consolidated, were compared to show their impact on the quality of the re-analysis. The general circulation model used in this study is shown to be capable of reproducing quite accurately the ocean dynamics of the Mediterranean Sea. The results have shown that the model solution is in agreement with data and observations, even though some parameterizations of the model should be improved (i.e. heat flux and mixing processes). The new implementation of a realistic water flux, proposed in this study, has improved the model solution so that re-analysis is possible. The study of the re-analysis produced shows that both products are sufficiently accurate for appropriate climate studies. Both assimilation schemes show good capabilities in correcting the solutions provided by the dynamical model. Moreover it has been shown the ability of both systems in retaining this information and projecting it in the future. Eventually, even for very complex non linear systems, with millions of prognostic variables, the equality between the Sequential Kalman Filter Approach and the Variational one as been demonstrated.